Infrared (IR) ship detection in unmanned aerial vehicles (UAV) imagery under maritime environments encounters challenges due to the complexity of sea-sky backgrounds. Current research efforts have not fully leveraged the unique characteristics of maritime scenes to distinguish this task from general IR target detection. To overcome these limitations, we propose a Two-Stage Sea-Sky Region-Segmentation-Guided IR Ship Enhancement and Detection (TIRSD) learning network, specifically designed for targeted ship enhancement and detection in maritime environments. The process is structured into two main stages: enhancement and detection. In the enhancement stage, pixel-level sea-sky region segmentation is accomplished via sea-sky line (SSL) detection, which is followed by precise IR ship enhancement and interference suppression, utilizing specially designed morphological filters. For the detection stage, the Bilateral Hierarchical Target Encoding Module (BHTEM) is introduced to encode both high- and low-dimensional features, ensuring optimal detail retention and extraction. Additionally, the Iterative Fusion Enhancement Midlayer (IFEM) with neighborhood reinforcement operations is presented. Furthermore, a newly controllable Local Discrete Attention Mechanism (LDAM) is utilized to focus on small ships, thereby improving detection efficiency. TIRSD surpasses the current leading methods in terms of accuracy, achieving a Mean Average Precision (mAP) of 76.9% on the proposed dataset and 52.4% on a public dataset. Comprehensive experimental results underscore TIRSD’s effective utilization of the unique characteristics of maritime environments, indicating promising application prospects.

Fast and accurate infrared (IR) sea–sky line (SSL) detection could greatly benefit the efficiency of target detection under maritime. However, the traditional SSL detection algorithms are slightly inferior in detection accuracy, while the convolutional neural network (CNN)-based algorithms have high standard hardware and dataset requirements, which are difficult to satisfy in some practical scenes. In this article, a novel concise and intuitive SSL detection algorithm named bisection window and homogeneity principle-based local contrast measure (BHLCM) is proposed. First, a bisection local contrast window (BLCW) is proposed based on the local contrast measure (LCM) and be used for the search of the patches that contain SSL segments along a set of variable preset vertical paths. Then, based on the analysis of the physical characteristics of areas near SSL, the homogeneity principle is proposed to remove the patches containing false SSL segments. Finally, the midpoints of the remaining SSL segments are used to fit the final SSL through random sample consensus (RANSAC). Experimental results based on four IR image sequences with more than 3000 IR images illustrate that compared with the state-of-the-art algorithms, BHLCM not only achieves precision comparable to the most accurate one but also is significantly ahead in speed among most of the algorithms compared. In addition, speed tests about BHLCM based on different hardware platforms show that real-time detection could still be achieved even without high-performance graphics cards. The code and dataset are available at BHLCM https://github.com/FJsRepo/BHLCM .

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.