An Infrared Ship Detection Algorithm based on YOLOv8n

Abstract

With the development of infrared technology, infrared imaging technology has also been widely used in marine ship detection. However, factors such as low contrast and high noise in infrared images result in poor detection performance. This article proposes an infrared ship detection algorithm based on YOLOv8n to address this issue. Firstly, by adding a small target detection layer, the detection accuracy of small target ships has been significantly improved. Secondly, a Focaler MPDIoU loss function was designed to address the issue of imbalanced sample categories and reduce excessive attention to easily classified samples. Finally, the introduction of a lightweight V7DownSampling downsampling module further improves detection accuracy and reduces the number of model parameters and model size. The experimental results show that the improved algorithm has improved the average accuracy on the publicly available InfiRay infrared ship dataset by 3.4 percentage points compared to the original YOLOv8n, while reducing the number of parameters by 11.8% and the model size by 8%, significantly improving detection accuracy and making it easy to deploy on resource limited platforms.