Non-Standard Clothing Detection in Electricity Scenes Based on Adaptive Training Samples Selection Neural Network

Abstract Standard clothing in the electricity scene is so important that it can validly prevent workers from injuries. Strengthen non-standard clothing detection can help people correct the bad habits of dressing. However, there are some challenges existing in standard clothing in electricity scenes. There are few numbers in some kinds of clothing (e.g. short sleeves or not wearing helmet) in electricity scenes. The colors of some personnel clothing are similar to those of the background of workshop, such as white helmets and white frames. To handle these issues, in this paper, a novelty clothing detection method in electricity scenes is proposed based on a policy of adaptive training samples selection. The number of clothing can be expanded by rectifying mosaic augmentation, and the information loss of the top features can be compensated by residual feature augmentations. Experimental results show that the model can automatically and accurately identify non-standard clothing from complex electricity scenes, and has better detection performance (mAP=0.433) compared with the other model (mAP=0.419).