Abstract: In order to improve the efficiency of container lock hole recognition and reduce the number of algorithm parameters and model size,a container lock hole recognition algorithm based on lightweight YOLOv5s is proposed.This algorithm replaces the Backbone feature extraction network of YOLOv5s with a lightweight neural network model MobileNetV3,and further optimizes the feature fusion structure of the neck part,which reduces the number of parameters and calculation amount of the model and improves the detection speed.The accuracy and efficiency of detection are improved by introducing the attention mechanism SimAM layer.After the model is reconstructed with different improvement methods,the training and testing are carried out on the self-built container lock hole data set,and the comparison test is carried out with the improved YOLOv5s.The results show that the size of the improved model is only 2.4MB,the average detection time of each image is 5.1ms,and the average detection accuracy is 97.3%.Compared with the original target detection model,the size of the model is reduced by 82.8%,and the detection speed is increased by 39%,showing strong real-time algorithm on the premise of ensuring high detection accuracy.

Key words: Machine vision, Container lock hole, YOLOv5s, Lightweight, Deep learning