Developing computer vision models for underwater environments is challenging due to the scarcity of high-quality annotated data, particularly for degraded conditions such as severe underexposure. In this work, we propose a progressive framework for synthetically generating realistic underexposed data to bridge this domain gap. We first introduce a rigorous multi-metric curation strategy to establish a ground-truth reference dataset of real underexposed images. We then propose three degradation modeling approaches, varying in complexity: RGB-Based Global Gamma Matching (GGM), Decorrelated Luminance Matching (DLM), and Perceptual Deep Feature Optimization (PDFO). The first two proposed methods are based on histogram alignment in different color spaces, while Our final proposed method, PDFO, leverages a pre-trained deep network to optimize for perceptual similarity. Qualitative and quantitative evaluations demonstrate that PDFO outperforms baseline methods generating the closest luminance distribution to real-world data. This framework provides a robust tool for augmenting underwater datasets, enabling more reliable object detection in low-light conditions.

Underwater object detection is challenged by visibility degradation caused by absorption, scattering, and turbidity. Although underwater image enhancement (UIE) is often used as a preprocessing step, prior work shows that enhancement can distort appearance and reduce detector performance. We revisit UIE from a data augmentation perspective and propose a mixed-domain training framework in which original images are paired with enhanced variants generated by four state-of-the-art UIE models: ACDC, AutoEnhancer, TUDA, and Semi-UIR. This design isolates the effect of enhancement-induced domain shifts while keeping the detector architecture and inference pipeline unchanged. Experiments on a curated subset of the USGS round goby dataset reveal that perceptual enhancement quality does not predict detection effectiveness: the enhancer with the lowest UIQM, UCIQE, and CCF scores yields the strongest mAP improvement, whereas the highest-scoring enhancers produce the weakest detection results due to overenhancement. We benchmarked the evaluation metrics across the original, enhanced, and mixed-domain datasets, and observed that Mixed-domain training compensates for the effects of enhancement and consistently improves mAP@50, F1-score, and detection accuracy, demonstrating that enhancementas-augmentation is an effective and lightweight strategy for improving the robustness of underwater object detection.

The reliability of object detection models fundamentally depends on training data quality, yet many benchmarks suffer from missing, confused, and inaccurate bounding boxes. This issue is particularly severe in the underwater domain, where visual degradations amplify annotation errors. The Real-world Underwater Object Detection (RUOD) dataset is a widely used benchmark that provides diverse real-world underwater scenes, but its annotations reflect the inherent difficulty of comprehensive annotation in such environments. To address this limitation, we present RUOD-R, a high-quality re-annotated version of RUOD developed using a rigorous protocol that combines image enhancement and quality-assured professional annotation. RUOD-R retains the original images, ensuring that performance differences stem solely from annotation quality. RUOD-R increases the total annotated instances by 3.5 & times;, with small object annotations increasing by over 140 & times;. Our comparative analysis reveals that many valid object instances, particularly small and visually degraded targets, were not annotated in the original dataset, and that matched boxes exhibit localization inaccuracy (mean IoU of 0.83 with RUOD-R). We evaluate detection performance using Faster R-CNN, RetinaNet, YOLOv11, and DynYOLO, spanning general-purpose and underwater-domain-specific architectures, on both datasets. Results show that RUOD-R is a substantially more challenging benchmark due to the increased object density and the predominance of small, occluded, and visually degraded targets. A controlled evaluation on paired images where both annotation sets contain the same objects suggests that annotation density, rather than coordinate differences, is the primary factor behind the performance gap. A flip-rate analysis reveals that up to 38% of detections counted as false positives on the original dataset are correct detections of valid objects. RUOD-R thus provides a more reliable benchmark for advancing underwater object detection. The new annotations and related metadata can be found at https://github.com/RSSL-MTU/RUOD-R

Underwater image enhancement has led to mixed performance when used in conjunction with object detectors. Some works report an improvement in object detection performance when used in conjunction with image enhancement, while other works show that enhancement degrades detection performance. In this work, we identify and attempt to quantify a confounding factor to reconcile these contradictory results: we show that poor image quality leads to low quality labels, which confounds performance assessments done with respect to these noisy labels. Application of image enhancement during the human labeling procedure recovers previously missed labels for less-biased performance assessment. We find that on the test set of the Rethinking general Underwater Object Detection (RUOD) dataset, re-annotation of enhanced images shows a mean increase of 9 labels per image, which leads to a 5 percentage point increase in precision during object detection with a YOLO-NAS model.

In recent years, Single-Image Super-Resolution (SISR) has gained significant attention in the geoscience and remote sensing community for its potential to improve the resolution of low-quality underwater imagery. This paper introduces MIMAR-Net (Multiscale Inception-based Manhattan Attention Residual Network), a new deep learning architecture designed to increase the spatial resolution of input color images. MIMAR-Net integrates a multiscale inception module, cascaded residue learning, and advanced attention mechanisms, such as the MaSA layer, to capture both local and global contextual information effectively. By utilizing multiscale processing and advanced attention strategies, MIMAR-Net allows us to handle the complexities of underwater environments with precision and robustness. We evaluate the model on three popular underwater image datasets, namely UFO-120, USR-248, and EUVP, and perform extensive comparisons against state-of-the-art methods. Experimental results demonstrate that MIMAR-Net consistently outperforms existing approaches, achieving superior qualitative and quantitative improvements in image quality, making it a reliable solution for underwater image enhancement in various challenging scenarios.

The widespread deployment of solar photovoltaic (PV) technology across diverse geographical regions, including cold and snowy climates, introduces distinct operational challenges. One of the most critical among these challenges is snow accumulation on PV panels, significantly hindering energy production efficiency [1] . Snow acts as a physical barrier, obstructing sunlight and preventing photons from reaching the active surface of the solar cells, thereby curtailing or completely halting electricity production. Therefore, timely snow shedding is crucial to restoring optimal energy generation and minimizing energy losses. Studies have shown that delayed snow removal can lead to energy losses of up to 34% [2] . Conventional approaches, such as manual visual inspections and fixed-camera systems [ 3 5 ], are often impractical, particularly for large-scale solar farms spanning vast areas. Manual inspections are inefficient and risky, often delaying snow-related energy loss detection, emphasizing the need for automated, data-driven monitoring systems. In our previous work [6] , we introduced a lightweight snow detection algorithm based on YOLOv11n, which analyzed drone imagery and achieved a precision of 0.93 and a recall of 0.75. This algorithm enabled the estimation of Snow Coverage Percentage (SCP), a critical metric for optimizing snow shedding strategies, reducing operational downtime, and enhancing overall energy yield. However, a key limitation of this real-time method lies in its reliance on a static pixel intensity threshold, making it vulnerable to inaccuracies under low-light or uneven lighting conditions. As a result, SCP predictions may be unreliable during dawn, dusk, or overcast weather, leading to potential misclassifications.

This paper presents the design, implementation, and evaluation of the Museum Guide Robot (MGR), an autonomous robotic system aimed at enhancing visitor experiences in museum environments. Driven by advancements in artificial intelligence (AI) and robotics, the MGR integrates sophisticated navigation and interaction technologies to facilitate personalized museum tours. Employing a combination of mechanical design features, including a lightweight chassis and Mecanum wheels for omnidirectional movement, the robot effectively navigates crowded spaces while maintaining stability and performance. Its electronic architecture incorporates motor drivers, cameras, LiDAR sensors to ensure precise navigation, obstacle avoidance, and screen with a real-time interface to interact with visitors. The system operates on the Robot Operating System (ROS), which supports modular development, enabling efficient integration of various functionalities, such as path planning and natural language processing. Simulation results demonstrate the MGR's high navigation accuracy and effective user interaction, while real-world testing highlights its potential to enrich the museum experience.

Underwater images often suffer from severe color distortion, low contrast, and reduced visibility, motivating the widespread use of image enhancement as a preprocessing step for downstream computer vision tasks. However, recent studies have questioned whether enhancement actually improves object detection performance. In this work, we conduct a comprehensive and rigorous evaluation of nine state-of-the-art enhancement methods and their interactions with modern object detectors. We propose a unified evaluation framework that integrates (1) a distribution-level quality assessment using a composite quality index (Q-index), (2) a fine-grained per-image detection protocol based on COCO-style mAP, and (3) a mixed-set upper-bound analysis that quantifies the theoretical performance achievable through ideal selective enhancement. Our findings reveal that traditional image quality metrics do not reliably predict detection performance, and that dataset-level conclusions often overlook substantial image-level variability. Through per-image evaluation, we identify numerous cases in which enhancement significantly improves detection accuracy—primarily for low-quality inputs—while also demonstrating conditions under which enhancement degrades performance. The mixed-set analysis shows that selective enhancement can yield substantial gains over both original and fully enhanced datasets, establishing a new direction for designing enhancement models optimized for downstream vision tasks. This study provides the most comprehensive evidence to date that underwater image enhancement can be beneficial for object detection when evaluated at the appropriate granularity and guided by informed selection strategies. The data generated and code developed are publicly available.

The 3rd Workshop on Maritime Computer Vision (MaCVi) 2025 addresses maritime computer vision for Unmanned Surface Vehicles (USV) and underwater. This report offers a comprehensive overview of the findings from the challenges. We provide both statistical and qualitative analyses, evaluating trends from over 700 submissions. All datasets, evaluation code, and the leaderboard are available to the public at https://macvi.org/workshop/macvi25.

Snow accumulation on solar panels presents a significant challenge to energy generation in snowy regions, reducing the efficiency of solar photovoltaic (PV) systems and impacting economic viability. While prior studies have explored snow detection using fixed-camera setups, these methods suffer from scalability limitations, stationary viewpoints, and the need for reference images. This study introduces an automated deep-learning framework that leverages drone-captured imagery to detect and quantify snow coverage on solar panels, aiming to enhance power forecasting and optimize snow removal strategies in winter conditions. We developed and evaluated two approaches using YOLO-based models: Approach 1, a high-precision method utilizing a two-class detection model, and Approach 2, a real-time single-class detection model optimized for fast inference. While Approach 1 demonstrated superior accuracy, achieving an overall precision of 89% and recall of 82%, it is computationally expensive, making it more suitable for strategic decision making. Approach 2, with a precision of 93% and a recall of 75%, provides a lightweight and efficient alternative for real-time monitoring but is sensitive to lighting variations. The proposed framework calculates snow coverage percentages (SCP) to support snow removal planning, minimize downtime, and optimize power generation. Compared to fixed-camera-based snow detection models, our approach leverages drone imagery to improve detection precision while offering greater scalability to be adopted for large solar farms. Qualitative and quantitative analysis of both approaches is presented in this paper, highlighting their strengths and weaknesses in different environmental conditions.

Underwater image enhancement is often perceived as a disadvantageous process to object detection. We propose a novel analysis of the interactions between enhancement and detection, elaborating on the potential of enhancement to improve detection. In particular, we evaluate object detection performance for each individual image rather than across the entire set to allow a direct performance comparison of each image before and after enhancement. This approach enables the generation of unique queries to identify the outperforming and underperforming enhanced images compared to the original images. To accomplish this, we first produce enhanced image sets of the original images using recent image enhancement models. Each enhanced set is then divided into two groups: (1) images that outperform or match the performance of the original images and (2) images that underperform. Subsequently, we create mixed original-enhanced sets by replacing underperforming enhanced images with their corresponding original images. Next, we conduct a detailed analysis by evaluating all generated groups for quality and detection performance attributes. Finally, we perform an overlap analysis between the generated enhanced sets to identify cases where the enhanced images of different enhancement algorithms unanimously outperform, equally perform, or underperform the original images. Our analysis reveals that, when evaluated individually, most enhanced images achieve equal or superior performance compared to their original counterparts. The proposed method uncovers variations in detection performance that are not apparent in a whole set as opposed to a per-image evaluation because the latter reveals that only a small percentage of enhanced images cause an overall negative impact on detection. We also find that over-enhancement may lead to deteriorated object detection performance. Lastly, we note that enhanced images reveal hidden objects that were not annotated due to the low visibility of the original images.

Underwater imagery often exhibits significant degradation and poor quality as compared to outdoor imagery. To compensate for this, Single-Image Super-Resolution (SISR) and enhancement algorithms are used to lessen this degradation and produce high-resolution images. In this study, we apply state-of-the-art Simultaneous Enhancement and Super-Resolution (SESR) and SISR models to different sets of downscaled images from the comprehensive RUOD dataset. We then conduct a qualitative and quantitative analysis of the upscaled and enhanced images using standard underwater image quality metrics (IQMs). Subsequently, we evaluate the robustness of the state-of-the-art YOLO-NAS detector against image sets with varying downscaled spatial resolutions. Lastly, we examine the impact that the SISR and SESR models has on YOLO-NAS detector performance. The findings reveal a decline in the detection performance on the downscaled test images and a further decline on the upscaled and enhanced images produced by SISR and SESR models, suggesting a negative relationship between such models and detection.

This paper explores the utilization of unmanned aerial vehicles, remote sensing, and machine vision to detect and estimate the size of contaminations in seawater. The study outlines the essential setups and adjustments to simulate this process in indoor and outdoor settings. The proposed system is designed to chart the optimal path for a quadrotor, utilized in these experiments, allowing it to navigate and pinpoint oil spill locations within the test arena. The drone successfully detects and accurately reports the oil spill's location across multiple trials. The results confirm the effectiveness of the proposed system in detecting and assessing oil spills, showcasing its potential in real-world applications.

Underwater imagery often suffers from severe degradation resulting in low visual quality and reduced object detection performance. This work aims to evaluate state-of-the-art image enhancement models, investigate their effects on underwater object detection, and explore their potential to improve detection performance. To this end, we apply nine recent underwater image enhancement models, covering physical, non-physical and learning-based categories, to two recent underwater image datasets. Following this, we conduct joint qualitative and quantitative analyses on the original and enhanced images, revealing the discrepancy between the two analyses, and analyzing changes in the quality distribution of the images after enhancement. We then train three recent object detection models on the original datasets, selecting the best-performing detector for further analysis. This detector is subsequently re-trained on the enhanced datasets to evaluate changes in detection performance, highlighting the adverse effect of enhancement on detection performance at the dataset level. Next, we perform a correlation study to examine the relationship between various enhancement metrics and the mean Average Precision (mAP). Finally, we conduct an image-level analysis that reveals images of improved detection performance after enhancement. The findings of this study demonstrate the potential of image enhancement to improve detection performance and provide valuable insights for researchers to further explore the effects of enhancement on detection at the individual image level, rather than at the dataset level. This could enable the selective application of enhancement for improved detection. The data generated, code developed, and supplementary materials are publicly available at: https://github.com/RSSL-MTU/Enhancement-Detection-Analysis.

In recent years, deep learning has significantly reshaped numerous fields and applications, fundamentally altering how we tackle a variety of challenges. Areas such as natural language processing (NLP), computer vision, healthcare, network security, wide-area surveillance, and precision agriculture have leveraged the merits of the deep learning era. Particularly, deep learning has significantly improved the analysis of remote sensing images, with a continuous increase in the number of researchers and contributions to the field. The high impact of deep learning development is complemented by rapid advancements and the availability of data from a variety of sensors, including high-resolution RGB, thermal, LiDAR, and multi-/hyperspectral cameras, as well as emerging sensing platforms such as satellites and aerial vehicles that can be captured by multi-temporal, multi-sensor, and sensing devices with a wider view. This study aims to present an extensive survey that encapsulates widely used deep learning strategies for tackling image classification challenges in remote sensing. It encompasses an exploration of remote sensing imaging platforms, sensor varieties, practical applications, and prospective developments in the field.

This paper presents the machine vision techniques implemented in an emulated environment that mimics the detecting process of oil spills in the ocean. An image processing algorithm is developed to achieve accurate detection and size measurement for studying oil spill cases. This study demonstrates the required setups and adjustments performed to mimic this process on a smaller scale, in a lab-based experiment. The proposed emulated system generates the required path for the quadrotor, used in this experiment, to maneuver around the arena and detect the oil spill. The drone successfully detected and accurately provided the location of the oil spot for several trails. For these attempts, the areas of the detected oil were calculated and compared. Some discussions were stated regarding some findings and exceptional cases. In general, the results attest to the efficacy of the proposed system.