AKS-YOLOv8: a lightweight and accurate network model for infrared ship detection

Abstract Combining infrared technology and artificial intelligence technology to achieve efficient ship detection is practically important for monitoring and scheduling of ships in harbours. Due to the infrared image texture features are fewer and the background is complex, these lead to the algorithm model having the problems of complex structure, high amount of memory consumption and not being easy to deploy. To address these problems, we propose a lightweight and accurate infrared ship detection model named AKS-YOLOv8, which is based on YOLOv8 design. Firstly, the Split Asymptotic Feature Pyramid Network (SA-FPN) is used in the neck network. It improves model to detect multi-scale infrared ship in large scenes while keeping the model lightweight. Secondly, we introduced the dynamic convolution module KernelWarehouse Dynamic Convolution (KConv) in the backbone network for enhancing the recognition of small targets in infrared images. And we also used FasterBlock instead of Bottleneck to reduce feature map redundancy and improve inference speed. Finally, we adopt Simple, Parameter-free Attention Module (SimAM) to improve the feature extraction ability for key information of infrared ships. To verify the effectiveness of AKS-YOLOv8, a comprehensive test is performed on the infrared ship target recognition database issued by Yantai iraytek Co., Ltd in comparison with some widely-used detection algorithms. The model precision of AKS-YOLOv8 is improved by 1%, and the parameters and model size are reduced by 37.2% and 32.1%, respectively. The results of the experiment indicate that AKS-YOLOv8 has successfully struck a favorable equilibrium between being lightweight and maintaining accuracy. It can provide a valuable technical reference for infrared ship detection technology.