KEYWORDS: Detection and tracking algorithms, Education and training, Data modeling, Target detection, Image enhancement, Deep learning, Head, Safety, Matrices, Binary data
Deep learning has made significant advancements in the field of computer vision in recent years. However, research in target detection technology has primarily focused on improving the data reuse rate, model generalization capability, and detection accuracy. In this study, we propose a data improvement technique called CutMix-Plus, which utilizes the YOLOv5n baseline network. We validate this technique using publicly available datasets. The mAP@0.5 of this algorithm is 0.892, and the mAP@0.5-0.95 is 0.548. These values represent improvements of 7.6% and 4.8% for mAP@0.5, and 5.6% and 4.6% for mAP@0.5-0.95, respectively, when compared to the CutMix and Mosaic algorithms. The experimental findings demonstrate that the approach can significantly improve the model's ability to generalize and improve its accuracy. Additionally, it can meet the requirements for detecting helmet wearing in construction scenarios.
Deep neural networks (DNNs), which have high accuracy prediction and stable network performance, have been widely deployed in various fields. However, the adversarial example, a sample of input data which has been modified very slightly in a way, may easily cause a DNN to maximize loss. Instead of white box attack being able to obtain gradient information, most DNN based systems in actual use can only be attacked by multiple queries. In this paper, we regard face recognition (FR) system as target, and propose a new method named SA-Attack to generate adversarial samples which cannot be distinguished by human within very limited queries. Experiments show that SA-Attack can successfully attack advanced face recognition models, including public and commercial solutions, which proves the practicability of our method.
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