AQSFormer: Adaptive Query Selection Transformer for Real-Time Ship Detection from Visual Images

Abstract

The Internet of Things (IoT) has emerged as a popular topic in both industrial and academic research. IoT devices are often equipped with rapid response capabilities to ensure seamless communication and interoperability, showing significant potential for IoT-based maritime traffic monitoring and navigation safety tasks. However, this also presents major challenges for maritime surveillance systems. The diversity of IoT devices and variability in collected data are substantial. Visual image ship detection is crucial for maritime tasks, yet it must contend with environmental challenges such as haze and waves that can obscure ship details. To address these challenges, we propose an adaptive query selection transformer (AQSFormer) that utilizes two-dimensional rotational position encoding for absolute positioning and integrates relative positions into the self-attention mechanism to overcome insensitivity to the position. Additionally, the introduced deformable attention module focuses on ship edges, enhancing the feature space resolution. The adaptive query selection module ensures a high recall rate and a high end-to-end processing efficiency. Our method improves the mean average precision to 0.779 and achieves a processing speed of 31.3 frames per second, significantly enhancing both the real-time capabilities and accuracy, proving its effectiveness in ship detection.