Infrared small target recognition in waterways based on YOLOv5 algorithm

YOLOv5 is one of the target detection algorithms with fast detection speed and high accuracy, but it has the problems of insufficient sensory field and low accuracy of small target detection. In order to solve above problems, an improved YOLOv5 network model, i.e., an improved YOLOv5-TI model based on the attention mechanism, is proposed. The attention module is added to the backbone network when extracting features to improve the target detection accuracy, and the input features are shifted windowed for self-attention calculation to effectively utilize the features and improve the small target detection accuracy; the proposed model YOLOv5-TI is experimented on the self-built inland infrared dataset, and the mAP value reaches 95.5%, and the results show that YOLOv5-TI can effectively improve the target detection accuracy. The inland vessels equipped with visual intelligent perception system can effectively identify the targets on water, and they have wide applications in the fields of surface exploration and autonomous search and rescue.