Ship Target Detection Algorithm Based on Improved YOLOX_s

Abstract

As an important part of ship intelligent navigation, improving the speed and accuracy of target detection can ensure the safe navigation of ships. Aiming at the low accuracy of ship target detection, an improved YOLOX_s network ship target detection algorithm is proposed. First, the Spatial Attention Module (SAM) is integrated into the YOLOX_s backbone network to focus on the target to be detected from the spatial dimension and improve the detection accuracy. Then, the Focal Loss loss function is used to replace the traditional BCE Loss loss function, which effectively alleviates the problem of unbalanced positive and negative samples of the single-stage target detector. The experimental results show that the detection accuracy of the improved YOLOX_s algorithm is 3.50% higher than the original algorithm, and the detection speed is only 1. 62ms lower. Without significantly reducing the detection speed, the ship target detection accuracy is effectively improved, which proves the effectiveness of the improved YOLOX_s algorithm.