Ship target detection in rainy days based on improved YOLOv8
TL;DR
Improved and optimizes a object detection network based on YOLOv8s, and builds a object detection network suitable for rainy days, which shows that the YOLOv8s-rain model has excellent detection performance, with an average accuracy rate of 78.9%, which is 1.1% higher than the standard YOLOv8s model.
Abstract
Ship target detection technology is a key link in intelligent navigation autonomous obstacle avoidance decision-making and maritime traffic supervision, and its performance is directly related to navigation safety and the efficiency of marine governance. In the rainy conditions, due to raindrop occlusion, light scattering and other factors, the visibility is significantly reduced, which leads to the limitation of ship detection performance. Aiming at this problem, this paper improves and optimizes a object detection network based on YOLOv8s, and builds a object detection network suitable for rainy days. The improvement mainly includes two points. One is to replace the standard convolution with deformable convolution in the (Backbone) C2f module; The second is to replace bilinear interpolation up-sampling with DySample dynamic upsampling in Neck module. At the same time, to fully extend the dataset, using the DerainCycleGAN method, images of ship targets covering different rainfall intensities were synthesized and constructed. The experimental comparison between the improved model and the standard YOLOv8s model shows that the YOLOv8s-rain model has excellent detection performance, with an average accuracy rate (mAP @ 0.5) of 78.9%, which is 1.1% higher than the standard YOLOv8s model.