Real-Time Infrared Sea–Sky Line Region Detection in Complex Environment Based on Deep Learning

Abstract

Fast and accurate infrared (IR) sea–sky line region (SSLR) detection can improve the early warning capability of the small targets that appear in the remote sea–sky junction. However, the traditional algorithms struggle to achieve high precision, while the learning-based ones have low detection speed. To overcome these problems, a novel learning-based algorithm is proposed; rather than detecting the sea–sky line first, the proposed algorithm directly provides SSLR, which mainly consists of three parts: Firstly, an IR sea–sky line region detection module (ISRDM) is proposed, which combines strip pooling and the connection mode of a cross-stage partial network to extract the features of the SSLR target, with an unbalanced aspect ratio, more specifically, thus improving the detection accuracy. Secondly, a lightweight backbone is presented to reduce the parameters of the model and, therefore, improve the inference speed. Finally, a Detection Head Based on the spatial-aware attention module (SAMHead) is designed to enhance the perception ability of the SSLR and further reduce the inference time. Extensive experiments conducted on three datasets with more than 26,000 frames show that the proposed algorithm achieved approximately 80% average precision (AP), outperforms the state-of-the-art algorithms in accuracy, and can realize real-time detection.