Multi-Attention Pyramid Context Network for Infrared Small Ship Detection

Abstract

In the realm of maritime target detection, infrared imaging technology has become the predominant modality. Detecting infrared small ships on the sea surface is crucial for national defense and maritime security. However, the challenge of detecting infrared small targets persists, especially in the complex scenes of the sea surface. As a response to this challenge, we propose MAPC-Net, an enhanced algorithm based on an existing network. Unlike conventional approaches, our method focuses on addressing the intricacies of sea surface scenes and the sparse pixel occupancy of small ships. MAPC-Net incorporates a scale attention mechanism into the original network’s multi-scale feature pyramid, enabling the learning of more effective scale feature maps. Additionally, a channel attention mechanism is introduced during the upsampling process to capture relationships between different channels, resulting in superior feature representations. Notably, our proposed Maritime-SIRST dataset, meticulously annotated for infrared small ship detection, is introduced to stimulate advancements in this research domain. Experimental evaluations on the Maritime-SIRST dataset demonstrate the superiority of our algorithm over existing methods. Compared to the original network, our approach achieves a 6.14% increase in mIOU and a 4.41% increase in F1, while maintaining nearly unchanged runtime.

FCNet: Flexible Convolution Network for Infrared Small Ship Detection

“…In this article, Precision (Prec) [46], Recall (Rec) [46], mIoU [46], F1 [46], and AUC [46] are selected as the evaluation indexes of algorithm accuracy, and Params [45], FlOPs [45], and FPS [45] are selected as the evaluation metrics of computational complexity.…”