Small unmanned aerial vehicles (UAVs) are difficult to detect in complex environments because of low pixel occupancy and strong background interference. This paper proposes ATU-Net, a YOLO11-based detector that integrates an enhanced multi-scale aggregation (EMA) backbone, a shuffle-fusion cross stage partial (SFCSP) neck module, a feature-sharing (FS) detection head, and Focaler-ShapeIoU. EMA combines progressive multi-scale feature aggregation (PMFA) for progressive detail–context aggregation with oriented asymmetric convolution (OAConv) for directional structural enhancement. SFCSP uses lightweight feature extraction and partial cross-stage fusion to reduce redundant computation, while the FS head shares feature transformations and prediction layers across scales and introduces multi-directional difference convolution to strengthen weak edge and texture-gradient responses. Focaler-ShapeIoU combines overlap-quality focusing with shape-aware geometric constraints. On DUT Anti-UAV and Det-Fly, ATU-Net improves YOLO11n by 2.7 and 3.1 percentage points in mAP50 and by 3.6 percentage points in mAP50–95 on both datasets. On VisDrone2019, the gains are 2.0 and 1.6 percentage points, respectively. Deployment on a Jetson Orin NX reaches 38.0 FPS under the tested 25 W configuration, showing that the accuracy gain is retained with real-time embedded inference despite additional runtime overhead.
Driven by the rapid expansion of the low-altitude economy, unmanned aerial vehicles (UAVs) have emerged as pivotal platforms for aerial perception. However, balancing detection accuracy and computational efficiency for micro-targets remains a formidable challenge. To address this, we propose LSO-YOLO, a novel real-time detector optimized for UAV-based small-object detection. Our primary contributions are threefold: first, a multi-scale dilated context aggregation (MDCA) module is designed to replace the final down-sampling convolution in the backbone. By employing sequential dilated depth-wise convolutions and a lightweight channel attention mechanism, the MDCA expands the receptive field and suppresses background noise, effectively mitigating the loss of fine-grained details caused by aggressive down-sampling; second, the C2f module in the neck is redesigned by substituting the second convolution in the bottleneck with parallel grouped convolutions, which enhances feature diversity and multi-scale representation while reducing computational overhead; finally, we optimize the overall architecture and introduce a lightweight down-sampling strategy to mitigate inter-channel interference. Experimental results on the VisDrone2019 dataset demonstrate that the proposed method improves mAP@0.5 and mAP@0.5:0.95 by 2.9 https://github.com/diapfer/LSOYOLO .
Reliable inspection of transmission line defects is critical for the safe and stable operation of modern power grids. Although UAV-based visual inspection combined with deep learning has been widely adopted, existing methods still suffer from high miss rates and limited accuracy when handling small-scale components and complex backgrounds. To address these limitations, this paper proposes TLD-YOLO, an enhanced object detection framework built upon the YOLOv5 baseline for fast and accurate transmission line defect inspection, which incorporates four key architectural improvements. First, a parameter-free Simple Attention Module (SimAM) is embedded into the backbone to strengthen multi-scale feature representation and improve sensitivity to small objects. Second, the Spatial–Channel Enhancement (SCE) module employs scale–channel interaction gating before multi-scale concatenation. This mechanism suppresses redundant scale responses and enhances discriminative feature fusion in dense-target and large-scale-variation scenarios. Third, the conventional neck network is replaced by a weighted Bi-directional Feature Pyramid Network (BiFPN) augmented with the Convolutional Block Attention Module (CBAM), enabling more effective cross-scale information aggregation while mitigating feature loss. Finally, a decoupled detection head separates classification from box regression and objectness prediction. The task-specific branches reduce task interference and improve detection accuracy. Experimental results on the self-constructed transmission line defect (TLD) dataset show that TLD-YOLO improves precision, recall, and mAP50 over YOLOv5 by 5.1
Small object detection in unmanned aerial vehicle (UAV) imagery holds significant application value across diverse domains, including urban security surveillance, agricultural crop monitoring, and disaster emergency response. However, existing methodologies encounter a fundamental trade-off dilemma between detection accuracy and computational efficiency, primarily attributed to extremely small target scales, complex background interference, and stringent computational resource constraints. To address these challenges, we propose AEI-DETR, which achieves superior detection accuracy compared to existing methods while substantially reducing model complexity through systematic architectural innovations. Specifically, we first design an adaptive spatial cross-stage backbone (ASCB) that reduces the complexity of global dependency modeling from quadratic O(L2) with respect to sequence length to linear O(L) through state space modeling, incorporating cross-stage feature aggregation and spatial coordinate enhancement mechanisms to enable efficient lightweight feature extraction. Subsequently, we develop an efficient lightweight feature neck (ELFN) that optimizes cross-scale feature fusion through adaptive fusion strategies within multi-feature modulation modules, leveraging depthwise separable convolutions to dramatically reduce computational overhead. Furthermore, we employ a bounding box regression strategy that combines internal constraints with distance optimization, constructing hierarchical optimization pathways through auxiliary bounding box mechanisms and point distance constraints to provide more stable and precise supervisory signals for small target localization. Experimental results on public datasets demonstrate that AEI-DETR significantly outperforms existing state-of-the-art methods.
Weeds in complex field environments are often small, densely distributed, and affected by severe background clutter, posing substantial challenges to the development of high-accuracy and low-complexity detection models. To balance detection accuracy with the requirements of lightweight deployment, this paper proposes a YOLOv11-based lightweight weed detection model, termed multi-level feature aggregation-based YOLO (MFA-YOLO). First, a gated progressive feature fusion (GPFF) module is proposed to enhance hierarchical feature interaction via learnable weighting and channel-wise gating. Second, an efficient feature aggregation (EFA) module strengthens fine-grained texture and semantic representation through lightweight attention-guided aggregation. Third, a P2-enhanced bidirectional attention feature pyramid network (P2-BAFPN) exploits shallow high-resolution features and bidirectional cross-scale fusion for small-scale weed perception. Finally, a lightweight shared detail-enhanced convolutional detection head (LSDECD) reduces head redundancy while improving detail-sensitive classification and localization in cluttered backgrounds. On the Lincoln Beet dataset, MFA-YOLO reduces the number of parameters, GFLOPs, and model size by 47.66
The intersection of adaptive machine learning and real-time systems presents one of the most pressing engineering challenges in modern computer vision. Online Evolutive Learning (OEL) enables models to improve continuously through environmental interaction during deployment. However, the computational overhead inherent to these adaptive mechanisms conflicts directly with the microsecond-to-millisecond latency budgets of real-time image processing pipelines. This survey provides a systematic review of the OEL landscape from the perspective of real-time image processing, which is organized along two principal axes: (i) the key algorithmic and hardware technologies that constitute the OEL—spanning continual learning, test-time adaptation, few-shot and meta-learning, self-supervised evolution, model compression, hardware-software co-design, generative AI and large vision-language model integration, and edge-cloud collaboration—and (ii) the downstream real-time image processing tasks to which OEL has been applied. For each technology and application area, we survey the state of the art, identify the fundamental conflicts between adaptability and real-time performance, and discuss algorithmic and architectural strategies to reconcile them. We conclude by proposing a unified evaluation protocol and identifying open challenges, including convergence guarantees under bounded latency, safe deployment in safety-critical systems, and the standardisation of real-time OEL benchmarks.
Recent years have seen growing interest in deploying computer vision (CV) applications at the network edge to meet real-time, bandwidth, and security constraints. This has increased the need to run multiple CV pipelines concurrently on resource- and power-constrained edge GPU accelerators, where shared resources, DVFS, and thermal throttling introduce complex interference effects that existing single-workload models do not capture. Benchmarking-based approaches are costly and do not scale across diverse combinations of workloads and platforms. This paper proposes a mathematical model to predict throughput and energy consumption for concurrently executing CV workloads on edge GPU accelerators. The model accounts for shared resource contention, host–device communication, DVFS, and thermal effects, and can be integrated into functional simulation frameworks for edge–cloud deployment studies. Validation across multiple edge platforms and representative CV workloads shows that the model predicts throughput and power consumption with an average error below 10%. The approach remains accurate under different compilation optimizations and precision levels, while requiring only minimal baseline benchmarking.
Face detection serves as a critical first step in many computer vision applications and has advanced significantly with the advent of convolutional neural networks (CNNs). Despite their effectiveness, CNN-based algorithms often require substantial computational resources, making hardware acceleration using FPGAs essential for real-time processing. FaceBoxes, one of the most accurate face detection algorithms, poses particular challenges for FPGA implementation due to its high computational complexity. In this paper, we propose an FPGA-based CNN inspired by FaceBoxes that significantly reduces hardware resource utilization and memory access requirements. The proposed network increases the number of layers while reducing filter sizes, and an optimized hardware architecture tailored to this network is presented. The design employs only 3 × 3 convolutional kernels, achieving a minimal accuracy degradation of 2.8
Accurate segmentation of the bolus and hyoid bone is essential for the automated detection of dysphagia in videofluoroscopic swallowing studies (VFSS). To address the challenges of blurred boundaries and substantial scale differences between the two target structures in VFSS images, this study considers feature extraction, multi-scale fusion, and branch interaction, and proposes a lightweight segmentation network, termed MSSA-Net, based on Mobile U-ViT. An edge detection module (ED) is introduced to enhance boundary features and efficiently extract critical information. During the downsampling process, a multi-scale self-attention mechanism (MSSA) is employed to capture dependencies among different spatial locations in the feature maps. In addition, a lightweight convolution—Transformer fusion module is designed to suppress redundant responses and integrate local details with global semantic information. Experimental results demonstrate that the proposed method can accurately and simultaneously track the bolus and hyoid bone. MSSA-Net-L achieves an mIoU of 83.64
The vision-based detections using deep learning need to localize and recognize the object of interests on continuous image sequence/video stream at low latency. Because of the intrinsic limitation of feature discriminability of non-salient objects inside images and over-complicated structures of present models, there is much space for improvement regarding detection efficiency and accuracy for such kind of targets. To address these problems, we propose a refined detection model named CDFI-YOLO. Our main contributions include: first, we introduce a Tri-Directional Attention (TDA) module into the backbone of the YOLOv11 baseline framework, modeling the spatial position information via a three-way attention mechanism to capture richer fine-grained features; second, we devise a Cross-Dimension Feature Interaction (CDFI) module to further refine the feature exchange procedure within our model, enhancing the recognition accuracy for non-salient objects; finally, we adopt a Lightweight Coupled Detection Head (LCDH), greatly reducing the parameter count of the baseline detection head thanks to its parameter-sharing GSConv structure, resulting in a 27.6
Low-altitude UAV obstacle detection is challenged by small obstacles that are easily disturbed by complex backgrounds, illumination changes, and motion blur, as well as by the strict computational constraints of onboard platforms. Although existing detection models can achieve high accuracy, they often suffer from large parameter sizes and redundant computations, making real-time edge device deployment difficult; meanwhile, direct lightweighting may weaken small-obstacle feature representation and lead to accuracy degradation. To address these issues, this paper designs a lightweight bidirectional extended path aggregation network (LBPAN) to enhance cross-scale feature fusion and introduces a balanced attention module (BAM) to improve small-object feature extraction. Meanwhile, a lightweight-C3k2 (L-C3k2) is proposed to reduce network redundancy. An improved Slim pruning method and a joint knowledge distillation strategy are adopted to achieve efficient model compression and accuracy compensation. On this basis, taking YOLOv11n as the baseline, a lightweight detection model lightweight bidirectional fusion YOLO (LBF-YOLO) is constructed and validated on a self-built low-altitude obstacle detection dataset. Experimental results show that, under significant lightweighting (parameters reduced from 2.6 to 1.4 M, FLOPs decreased from 6.6 to 4.4 G), mAP_0.5 increases from 85.3 to 88.4 mAP_0.5:0.95 rises from 62.8 to 66.4
Accurate safety-helmet detection in underground coal mines remains a critical challenge due to extreme environmental factors and the prevalence of small-scale, partially occluded targets. To address these bottlenecks, this paper proposes LCV-YOLO, a lightweight and robust detection framework specifically architected for complex mining environments. We re-engineer the backbone via a tailored LC-C2f-LSK module to dynamically expand the receptive field for minute targets, and introduce a CGA Fusion mechanism at the neck to suppress mining noise while adaptively enhancing target semantics. Furthermore, an efficiency-driven VoV-GSCSP module is implemented to prioritize feature reuse under strict computational constraints. Extensive experiments demonstrate that LCV-YOLO outperforms the YOLOv8 baseline with a 5.7
Lane line detection, object detection, and drivable area segmentation serve as the core tasks of autonomous driving environment perception. The accuracy and real-time performance of this perception directly impact the operational safety of intelligent transportation systems. Existing multi-task vision models typically employ a parallel branch structure, resulting in inadequate information sharing and guidance among tasks. In occluded or blurred areas, the accuracy is relatively low, which can further give rise to the risk of traffic accidents and pose a potential threat to public safety. Additionally, it is challenging to achieve a balance between detection accuracy and inference latency, which may also induce safety risks. To tackle these issues, this paper presents a lightweight multi-task learning framework for safety-critical autonomous driving perception. This framework integrates the three aforementioned tasks into a unified model to concurrently enhance real-time processing efficiency and perception reliability. First, Cross-Task Attention Fusion (CTAF) is proposed. Through the Cross-Task Attention Block (CTAB), the collaborative modeling capability of lane line detection and drivable area segmentation is strengthened. Second, Dynamic Path Fusion with Transformer (DPFT) is introduced to dynamically generate the fusion weights of feature maps, thereby enhancing the model’s adaptability to different driving scenarios. Finally, a semantic boundary detection auxiliary task is introduced to improve edge perception and the localization accuracy of lane lines and road boundaries. The overall network employs a shared encoder-task-specific decoder structure and utilizes ELAN-Net as the backbone network for feature extraction. Experimental results show that the proposed method achieves an inference speed of 78 FPS while maintaining a lightweight design and improving multi-task perception accuracy. It strikes a balance between accuracy and real-time performance in multi-task perception, thereby supporting safety-critical perception by reducing perception latency and improving perception reliability.
Timely and accurate detection of steel surface defects is crucial for ensuring industrial product quality and production safety. However, existing methods often struggle to balance detection accuracy, inference speed, and model lightweightness, especially under complex industrial conditions with multi-scale defects. To address this issue, this work proposes EG-YOLO, a lightweight and efficient detection framework based on YOLO11n for real-time steel surface defect inspection. Specifically, the Efficient Multi-Scale Attention (EMA) module is embedded into the backbone to enhance multi-scale feature representation through cross-spatial learning, while Generalized Intersection over Union (GIoU) loss is adopted to improve bounding box regression and localization of small and irregular defects. Experiments on the NEU-DET dataset show that EG-YOLO achieves 79.7
Semantic segmentation of point clouds is a fundamental task in environmental perception for autonomous driving. Due to the irregularity and sparsity of LiDAR data, efficiently processing large-scale unstructured point clouds remains a significant challenge. This paper presents LERPNet, a lightweight range-view-based semantic segmentation network. Specifically, we replace the Stem layer in FRNet with the proposed FD-Stem, which improves feature representation while reducing computational complexity. The proposed Adaptive Spatial-Channel Attention Enhancement module introduces long-range modeling capability with limited additional parameters, enabling effective learning of both spatial and channel-wise representations. Furthermore, the Range Point Nearest Neighbor Fusion Head alleviates the many-to-one mapping issue with lower inference latency, thereby improving segmentation performance. Extensive experiments demonstrate that LERPNet achieves competitive mIoU of 70.1
To address the issue of low detection accuracy for hazardous items caused by complex backgrounds, severe occlusion, multi-scale variations, and diverse poses in X-ray security inspection images, this study adopts the YOLOv12 network as the baseline model. The ADown module is introduced to improve downsampling efficiency while maintaining lightweight performance. Additionally, the BN-CGLU (batch normalization convolutional gated linear unit) is incorporated into the A2C2f module to enhance the model’s nonlinear representation capability. Furthermore, the SEAM (separated and enhancement attention module) attention mechanism is integrated into the detection head to improve detection precision under occlusion conditions. Experiments conducted on the public SIXray dataset demonstrate that, compared to YOLOv12, the proposed method reduces computational complexity by 15.5
UAV-based infrared small target detection is challenging due to extremely small target size, weak texture information, complex background clutter, and strict real-time requirements. To address these issues, this paper proposes EHR-Net, a real-time Edge-Enhanced Hierarchical Residual Network built upon the YOLOv11 framework. Specifically, an Edge-Information Enhanced Stem (EIEStem) is introduced to preserve shallow details during early downsampling with limited computational overhead. A Residual MetaFormer-CGLU (RMF-CGLU) module is designed to enhance backbone feature representation through efficient context modeling and gated channel interaction. In addition, a Hierarchical Residual Aggregation Module (HRAM) is incorporated to improve multi-scale feature fusion and semantic aggregation. By integrating these components, EHR-Net can effectively enhance weak target responses and alleviate background interference while maintaining efficient inference. Experimental results on the HIT-UAV and NUDT-SIRST datasets show that EHR-Net improves mAP@0.5 by 4.9
To address the challenges of large-scale variations, weak textures, and parameter redundancy in steel surface defect detection, a real-time lightweight hybrid multi-scale adaptive feature learning network, termed HMA-DETR, is proposed based on RT-DETR. First, a hybrid gated aggregation network (HGANet) is designed to enhance defect-related spatial–channel representation through multi-scale spatial modeling and lightweight channel refinement, thereby improving the representation of defects with diverse shapes and textures. Second, a triple re-parameterized reconstruction stack (TriRepStack) is introduced after multi-scale feature fusion to reconstruct fused features and reduce semantic discrepancies across different feature levels, while preserving inference efficiency through structural re-parameterization. Finally, an adaptive sampling convolution (ASConv) is introduced to perform learnable offset-guided sampling and feature rearrangement during spatial reduction, aiming to alleviate information loss for small and irregular defects. Experimental results on the GC10-DET and NEU-DET datasets show that HMA-DETR improves mAP _50 by 3.8
Real-time object detection in continuously streaming image and video data demands that models remain both highly accurate and computationally frugal, yet existing lightweight detectors either sacrifice multi-scale robustness by narrowing network width or incur unnecessary overhead through scene-agnostic feature fusion strategies. To address these limitations, we propose Gated Adaptive Fusion Detector (GAF-Det), a single-stage detector whose neck replaces fixed-weight concatenation with a Lightweight Gated Feature Fusion (LGFF) module that dynamically computes per-channel fusion weights conditioned on both the semantic and spatial streams, and whose backbone employs a Depthwise Separable Downsampling (DSDown) block that combines a learnable depthwise separable branch with a max-pooling branch to preserve fine-grained spatial detail at roughly 88 _50 on MS COCO with 3.1M parameters and 14.8 GFLOPs, running at 36 FPS on the NVIDIA Jetson AGX Xavier, demonstrating that input-adaptive feature fusion and efficient downsampling together yield a favorable accuracy–latency trade-off for general-purpose edge deployment.
Underwater object detection is of significant practical importance for marine resource exploration, underwater robotic navigation, and marine ecological monitoring. However, underwater images are often severely degraded by light attenuation and scattering, suspended particulates, and complex background interference. These factors lead to low contrast, strong color distortion, and blurred object boundaries, which collectively pose substantial challenges to reliable object detection. To address these issues, this paper proposes an object detection framework specifically designed for underwater environments. First, we develop a dynamic image feature fusion backbone module, termed DyC2F (dynamic lightweight convolution to fusion). By embedding dynamic convolution into a dual-path architecture, DyC2F enhances the modeling of local textures and weak boundary features while maintaining low computational complexity. Second, a three-dimensional attention fusion mechanism, referred to as TDAF (three-dimensional attention fusion), is introduced to adaptively enhance multi-scale underwater features across channel, scale, and spatial dimensions, thereby improving the detectability of low-contrast small objects. Finally, the detection head is further optimized by incorporating a dynamic activation function, a task-decoupled alignment module, and a quality focal loss, which effectively mitigates feature conflicts between classification and localization in complex underwater backgrounds and improves prediction stability. Experimental results on representative underwater datasets demonstrate that the proposed method consistently outperforms mainstream approaches in terms of detection accuracy, robustness, and small-object recognition. Moreover, the proposed framework achieves a processing speed of 188.7 frames per second (fps), satisfying real-time detection requirements in complex underwater scenarios.
