The rapid development of computer image processing has greatly improved the progress of artificial intelligence. When big data and distributed machine learning interact, the accuracy and efficiency of algorithm recognition can be greatly improved, but its security cannot be guaranteed in terms of privacy data protection. The traditional horizontal federated learning algorithm FedAvg can perform model training under the premise of protecting user data, but the security vulnerabilities in the parameter interaction process and the local data training process may be exploited by internal or external attackers. In order to balance the efficiency and security of the algorithm, this paper proposes an image classification model SGX-FedAvg. The model adds Intel SGX based on hardware encryption mechanism on the basis of horizontal federated learning FedAvg, which loses a small part of the algorithm efficiency of federated learning and adds reliable security to its model. In this paper, two data sets, Cifar-10 and Mnist, are selected for training. The model proposed in this paper is compared with Native local distributed machine learning and FedAvg horizontal federated learning. It is analyzed that SGX-FedAvg has good performance in execution efficiency and security within acceptable overhead, and its feasibility is verified. Finally, it points out which fields the model can be applied to.
In recent years, artificial intelligence applications have been on the rise. Many enterprises have embraced digital transformation and have established new business models based on artificial intelligence and the Internet of Things, such as the telerehabilitation industry. The companies may utilize sensors or cameras to collect user data, and data mining is applied to discover insights for doctors' aids. This paper establishes a novel two-stage data mining model combining gait recognition and sequential pattern mining. In the first stage, a particular computer vision application, gait recognition, identifies possible diseases using the subject's walking postures. The gaits in a video can be converted to a temporal sequence according to user-defined events. For example, (normal gait, Parkinsonian gait, normal gait) is a temporal sequence in which the identified gaits are arranged by temporal orders in the sequence. In the second stage, after collecting a dataset of temporal sequences, the frequent patterns are discovered by sequential pattern mining. Our preliminary experiment collected 30 samples from the real world and demonstrated the model's feasibility.
Abstract—Aiming at the problems of long reasoning time and high hardware requirements of workers ' helmet wearing detection model in existing construction sites, a lightweight helmet wearing detection algorithm based on improved FastestDet is proposed. Firstly, the FASTDET backbone network is optimized to further reduce the number of parameters while ensuring accuracy. Secondly, the spatial pyramid pooling module is changed from SPP to SimSPPF proposed by Meituan YOLOv6, which accelerates the recognition speed. Finally, the EMA attention mechanism is introduced at different locations to enhance the perception ability of the model. The experimental results show that the recall rate and mAP of the improved FastestDet algorithm are 1.3 % and 1.7 % higher than FastestDet, respectively. And the inference speed is faster than other models.
Infrared thermal imaging is capable of operating under various lighting conditions, unaffected by external factors, but it often suffers from issues like low contrast and blurry details. This article introduces an infrared image enhancement algorithm based on multi-scale guided filtering. The algorithm employs guided filtering to split the captured infrared image into a base layer and a detail layer. The base layer undergoes non-linear compression via segmented histogram equalization to enhance image contrast, while multi-scale guided filtering extracts diverse detail information from the image, which is then merged into a new detail layer to amplify fine details. Finally, the base layer and detail layer are combined to complete the enhancement of the infrared image. Experimental results demonstrate that this algorithm surpasses suboptimal approaches by 15.2% in terms of Enhancement Measure Evaluation(EME) and enhances the peak signal-to-noise ratio (PSNR) by 7.9%.
Strong infrared radiation interference is a common type of interference in optoelectronic countermeasures. An optical system exposed to strong light would disrupt the acquisition of image information and even damage the device. In this paper, we aim to design an infrared system for object detection under the circumstances of strong infrared radiation interference in order to enhance the countermeasure capability of the optoelectronic system. Firstly, we introduce the Wave-front Coding (WFC) technique to attenuate the damage caused by strong infrared radiation interference. Secondly, we use L₂ (Tikhonov) regularization to restore the blurred image caused by wavefront coding and obtain a clear low-light image. Thirdly, we use Recursively Separated and Weighted Histogram Equalization (RSWHE) for image enhancement to improve its contrast. Finally, we use the YOLOv8 algorithm for object detection. Experiments on real infrared images show that this scheme has outstanding performance under strong infrared radiation interference.
