Ship Detection with Optical Image Based on Attention and Loss Improved YOLO
Yao Chen, Biao Zhang, Zhen Li, Yuanyuan Qiao · 2022
Object detection is a critical research topic in computer vision. As a subtask of object detection, Ship detection has important research significance. Most ship detection research is based on SAR (Synthetic Aperture Radar) images in the existing research. However, compared with SAR images, optical images have more image feature information, which can assist the algorithm to better learn ship features. In addition, the research of optical image ship detection has more important commercial value. This paper conducts optical image ship detection experiments, applies the YOLO algorithm to the ship detection task, introduces the attention mechanism to transform the residual block in the DarkNet-53 network, and achieves more excellent performance. At the same time, this paper optimizes the recently proposed CIoU loss function which is better than the ln−norm loss function and presents the AIoU loss function on this basis. By increasing the area penalty term, the AIoU loss improves performance while converging speed compared with CIoU loss. It is applied to ship detection in the optical image and compared with GIoU, DIoU, and CIoU loss functions, and it has achieved better results than them in the bounding box regression of ship detection.