Recent deep models trained on large-scale RGB datasets lead to considerable achievements in visual detection tasks. However, the training examples are often limited for an infrared detection task, which may deteriorate the performance of deep detectors. In this paper, we propose a transfer approach, Source Model Guidance (SMG), where we leverage a high-capacity RGB detection model as the guidance to supervise the training process of an infrared detection network. In SMG, the foreground soft label generated from the RGB model is introduced as source knowledge to provide guidance for cross-domain transfer. Additionally, we design a Background Suppression Module in the infrared network to receive the knowledge and enhance the foreground features. SMG is easily plugged into any modern detection framework, and we show two explicit instantiations of it, SMG-C and SMG-Y, based on CenterNet and YOLOv3, respectively. Extensive experiments on different benchmarks show that both SMG-C and SMG-Y achieve remarkable performance even if the training set is scarce. Compared to advanced detectors on public FLIR, SMG-Y with 77.0% mAP outperforms others in accuracy, and SMG-C achieves real-time detection at a speed of 107 FPS. More importantly, SMG-Y trained on a quarter of the thermal dataset obtains 74.5% mAP, surpassing most state-of-the-art detectors with full FLIR as training data.

Robust detection of infrared slow-moving small targets is crucial in infrared search and tracking (IRST) applications such as infrared guidance and low-altitude security; however, existing methods easily cause missed detection and false alarms when detecting infrared small targets in complex low-altitude scenes. In this article, a new low-altitude slow-moving small target detection algorithm based on spatial-temporal features measure (STFM) is proposed. First, we construct a circular kernel to calculate the local grayscale difference (LGD) in a single image, which is essential to suppress low-frequency background and irregular edges in the spatial domain. Then, a short-term energy aggregation (SEA) mechanism with the accumulation of the moving target energy in multiple successive frames is proposed to enhance the dim target. Next, the spatial-temporal saliency map (STSM) is obtained by integrating the two above operations, and the candidate targets are segmented using an adaptive threshold mechanism from STSM. Finally, a long-term trajectory continuity (LTC) measurement is designed to confirm the real target and further eliminate false alarms. The SEA and LTC modules exploit the local inconsistency and the trajectory continuity of the moving small target in the temporal domain, respectively. Experimental results on six infrared image sequences containing different low-altitude scenes demonstrate the effectiveness of the proposed method, which performs better than the existing state-of-the-art methods.

Robust infrared (IR) small target detection is critical for infrared search and track (IRST) systems and is a challenging task for complicated backgrounds. Current algorithms have poor performance on complex backgrounds, and there is a high false alarm rate or even missed detection. To address this problem, a weighted local coefficient of variation (WLCV) is proposed for IR small target detection. This method consists of three stages. First, the preprocessing stage can enhance the original IR image and extract potential targets. Second, the detection stage consists of a background suppression module (BSM) and a local coefficient of variation (LCV) module. BSM uses a special three-layer window that combines the anisotropy of the target and differences in the grayscale distribution. LCV exploits the discrete statistical properties of the target grayscale. The weighted advantages of the two modules complement each other and greatly improve the effect of small target enhancement and background suppression. Finally, the weighted saliency map is subjected to adaptive threshold segmentation to extract the true target for detection. The experimental results show that the proposed method is more robust to different target sizes and background types than other methods and has a higher detection accuracy.

Superpixel segmentation has become a crucial pre-processing tool to reduce computation in many computer vision applications. In this paper, a superpixel extraction algorithm based on a seed strategy of contour encoding (SSCE) for infrared images is presented, which can generate superpixels with high boundary adherence and compactness. Specifically, SSCE can solve the problem of superpixels being unable to self-adapt to the image content. First, a contour encoding map is obtained by ray scanning the binary edge map, which ensures that each connected domain belongs to the same homogeneous region. Second, according to the seed sampling strategy, each seed point can be extracted from the contour encoding map. The initial seed set, which is adaptively scattered based on the local structure, is capable of improving the capability of boundary adherence, especially for small regions. Finally, the initial superpixels limited by the image contour are generated by clustering and refined by merging similar adjacent superpixels in the region adjacency graph (RAG) to reduce redundant superpixels. Experimental results on a self-built infrared dataset and the public datasets BSD500 and 3Dircadb demonstrate the generalization ability in grayscale and medical images, and the superiority of the proposed method over several state-of-the-art methods in terms of accuracy and compactness.