Abstract: In order to solve the problems of large proportion of small targets in optical remote sensing data, the aspect ratio is large, and multiple targets are closely arranged and difficult to detect,we present an optical remote sensing small ship target detection algorithm based on the YOLO V5(You Only Look Once V5), UPCBAM- RYOLO V5 (Upsampling Convolutional Attention Block Module-RYOLO V5) algorithm. An up-sampling attention mechanism module is designed to enhance the feature extraction ability of small size targets. The rotation angle loss is introduced into the frame regression formula to improve the algorithm’s perception ability of the ship’s appearance and direction. Based on the experiment of small ship target datasets composed of GF1 and GF2, the results show that the UPCBAM-RYOLO V5 algorithm model improves the positioning accuracy and classification accuracy of small ship target detection. The P value, R value, and MAP(Mean Average Precision) value reach 93%, 94%, and 95% respectively, which are 3%, 7%, and 7% higher than the original YOLO V5 model. In the upsampled attention-mechanism module added location ablation experiment to the network, the results show that compared with the addition of UPCBAM in Backbone and Prediction, the addition of UPCBAM in Neck has the greatest influence on the detection of small target ships in remote sensing images. The performance is the best, with P value, R value, and MAP value increased by 5%, 4%, and 2%, respectively. The UPCBAM-RYOLO V5 model is proved to have a certain research significance in optical remote sensing ship small target detection.

Key words: optical remote sensing data, small ship targets, upsampling convolutional attention block module-R you only look once V5(UPCBAM-RYOLOV5) algrithom, upsampling convolutional attention block module, rotation angle