Fast Multi-scale Target Detection in Apron Based on Variable Resolution Mechanism

In the apron target detection, the target size is very small and a large number of fine particle characteristics are lost in the compression process, and this results in the recognition errors. Fine granularity directly affects the accuracy of target recognition. However, due to the limited energy consumption and computational power of equipment, and the insufficient feature extraction of small targets by universal target detection algorithms, the improvement of speed and accuracy of such algorithms is restricted. This paper presents a real-time detection algorithm YOLO for apron target detection. By using the variable resolution mechanism, the algorithm detects the compressed image once, and then identifies the difficult targets twice. Finally, the unimportant channels in the network are trimmed based on the scaling factor of the batch normalization layer. The trimmed and slimmed algorithm has a higher speed. The test results on the network public data set and the self-built data set show that the MRMY algorithm improves the mAP by about 21% compared with the YOLO V4 algorithm, and the detection speed is 84 FPS, which is similar to the 83 FPS of YOLO V4.