The performance of existing object detection algorithms significantly degrades when applied to low-resolution infrared (IR) images captured by unmanned aerial vehicles (UAVs), which suffers from slow inference speed, low detection precision, and redundant network parameters. To tackle these issues, this paper proposes a lightweight real-time object detection network for UAV IR images, termed SGA-YOLO, which is designed based on the you only look at once version 8n (YOLOv8n) framework. First of all, the efficient SENetV2-neck enhances the correlation between different channels, which realizes efficient multi-scale feature fusion and improves detection precision. Subsequently, the lightweight S2GM backbone combines ShuffleNetV2-stride2 and C2f_Ghost modules, which significantly reduces the network parameters and increases inference speed. Finally, the adaptive fine-grained channel (AFGC) attention mechanism is coupled to further enhance detection precision and effectively mitigate background interference. Compared with the YOLOv8n, SGA-YOLO achieves a 27% reduction in network parameters, a 4.7% increment in precision, a 2.5% increment in recall rate, a 30.86% reduction in GFLOPs, a 16.3% increment in FPS, a 3.50% increment in [email protected], and a 1.3% increment in [email protected]:0.95. In addition, it supports deployment on resource-constrained embedded system, offering a new perspective on designing lightweight UAV IR object detection networks for real-world applications in intelligent transportation systems. Our codes are available athttps://github.com/gepengqiang2025/SGA-YOLO.

Nowadays, infrared target tracking has been a critical technology in the field of computer vision and has many computational social system-related applications, such as urban security, pedestrian counting, smoke and fire detection, and so forth. Unfortunately, due to the absence of detailed information such as texture or color, it is easy for tracking drift to occur when the tracker encounters infrared targets that vary in shape or size. In order to address this issue, we present a twofold structured features-based Siamese network for infrared target tracking. Above all, a novel feature fusion network is proposed to make full use of both shallow spatial information and deep semantic information in a comprehensive manner, so as to improve the discriminative capacity for infrared targets. Then, a multitemplate update module is designed to effectively deal with interferences from target appearance changes which are prone to cause early tracking failures. Finally, both qualitative and quantitative experiments are implemented on VOT-TIR 2016 and GTOT datasets, which demonstrates that our method achieves the balance of promising tracking performance and real-time tracking speed against other state-of-the-art trackers.

The quality of underwater imaging is significantly degraded by light scattering and absorption due to water body and suspended particles. To address the issues of color distortion and contrast degradation, we propose a novel underwater image restoration method based on constrained color compensation and background light color space-based haze-line model. Our method begins by applying the constrained color compensation approach to provide targeted intensity adjustments for attenuated color channels. This process corrects color distortion while simultaneously mitigating the risks of overcompensation and insufficient color saturation. Subsequently, the haze-line model is employed in the transformed background light color space to restore the underwater image. Specifically, the color corrected underwater image is transformed to a novel color space, where the background light intensity serves as the origin. In this color space, all haze lines are identified by grouping pixels with similar color characteristics through the superpixel clustering method. Then, the transmission distribution can be estimated based on the haze-line model. Finally, the scattered light components are removed by applying the underwater descattering model with the estimated transmission distribution to the luminance channel of the color corrected underwater image. Comparative experiments implemented on the UIEB and UCCS underwater image datasets demonstrate the superiority of the proposed method in terms of color correction and contrast enhancement when compared with state-of-the-art underwater image restoration techniques. Our codes are available athttps://github.com/MinjieWan/C3HLM.

Maritime infrared (IR) small target detection in dense sunglint environments has always been challenging. It is difficult for existing methods to distinguish the small target from dense and strong sea clutter because the sea clutter caused by the sunglints has similar spatial-temporal characteristics to small targets. This paper proposes a method based on appearance stability and depth-normalized motion saliency (AS-DNMS) for small target detection in IR videos or sequences. First, on each frame of the IR video, the isotropic salience measure (ISM) based on principal curvature filtering is proposed for target enhancement, and an adaptive threshold is used to extract a stable number of candidate targets. Then, we use improved pipeline filtering to form trajectory chains of the candidate targets extracted from consecutive frames. To improve the matching accuracy of pipeline filtering, we propose an inverse optical flow method to predict the local images of the candidate targets. Third, the motion vectors of the candidate targets are extracted and depth-normalized. According to the principle of projection perspective, a depth normalization model based on the position of the sea-sky line is established, providing a simple and low-cost solution for acquiring depth information of maritime targets under sea-sky backgrounds. Finally, the appearance stability measure (ASM) and the depth-normalized motion saliency measure (DNMSM) are calculated to construct the AS-DNMS joint feature. A double asymptote decision function is employed to determine the real target. The experimental results show that our method has better detection performance in dense sunglint environments than the baseline methods.

Realizing robust infrared small target detection in complex backgrounds is of great essence for infrared search and tracking (IRST) applications. However, the high-intensity structures in background regions, such as the sharp edges, make it a challenging task, especially when the target is with low signal-to-clutter ratio (SCR). To address this issue, we propose an infrared small target detection method using local contrast-weighted multidirectional derivative (LCWMD). It is a robust detector that comprehensively considers the target property, background information, and the relation between them. First, we consider the approximate isotropy of the infrared small target and present a new multidirectional derivative with penalty factors based on the Facet model to develop the target salience in the local region. Second, a dual local contrast fusion model with the trilayer design is introduced to amplify the difference between the target and the background, so as to further suppress the high-intensity structural clutters. Finally, the LCWMD map is obtained by weighting the above two filtered maps, after which an adaptive segmentation operation is applied to accomplish the target detection. The results of comparative experiments implemented on real infrared images demonstrate that our method outperforms other state-of-the-art detectors by several times in terms of SCR gain (SCRG) and background suppression factor (BSF).

Infrared (IR) small target detection is one of the most fundamental techniques in the infrared search and track (IRST) system. Due to the interferences caused by background clutter and image noise, conventional IR small target detection algorithms always suffer from a high false alarm rate and are unable to achieve robust performance in complex scenes. To accurately distinguish IR small target from the background, we propose a total variation (TV)-based interframe infrared patch-image model that regards the long-distance IR small target detection task as an optimization problem. First, the input IR image is converted to a patch-image that consists of a sparse target matrix and a low-rank background matrix. Then, the interframe similarity of target appearance is utilized to impose a temporal consistency constraint on the target matrix. Next, a TV regularization term is proposed to further alleviate the false alarms generated by noise. Finally, an alternating optimization algorithm using singular value decomposition (SVD) and accelerated proximal gradient (APG) is designed to mathematically solve the proposed model. Both qualitative and quantitative experiments implemented on real IR sequences demonstrate that our model outperforms other traditional IR small target methods in terms of the signal-to-clutter ratio gain (SCRG) and the background suppression factor (BSF).

The precision of infrared (IR) small target tracking is seriously limited due to lack of texture information and interference of background clutter. The key issue of robust tracking is to exploit generic feature representations of IR small targets under different types of background. In this letter, we present a new IR small target tracking method via compressive convolution feature (CCF) extraction. First, a Gaussian curvature-based feature map is calculated to suppress clutters so that the contrast between target and background can be obviously improved. Then, a three-layer compressive convolutional network, which consists of a simple layer, a compressive layer, and a complex layer, is designed to represent each candidate target by a CCF vector. Based on the proposed mechanism of feature extraction, a support vector machine (SVM) classifier with continuous probabilistic output is trained to compute the likelihood probability of each candidate. Finally, the long-term tracking for IR small target is implemented under the framework of the inverse sparse representation-based particle filter. Both qualitative and quantitative experiments based on real IR sequences verify that our method can achieve more satisfactory performances in terms of precision and robustness compared with other typical visual trackers.

Target tracking based on unmanned aerial vehicle (UAV) video is a significant technique in intelligent urban surveillance systems for smart city applications, such as smart transportation, road traffic monitoring, inspection of stolen vehicle, etc. In this paper, a computer vision-based target tracking algorithm aiming at locating UAV-captured targets, like pedestrian and vehicle, is proposed using sparse representation theory. First of all, each target candidate is sparsely represented in the subspace spanned by a joint dictionary. Then, the sparse representation coefficient is further constrained by an L2regularization based on temporal consistency. To cope with the partial occlusion appearing in UAV videos, a Markov random field (MRF)-based binary support vector with contiguous occlusion constraint is introduced to our sparse representation model. For long-term tracking, the particle filter framework along with a dynamic template update scheme is designed. Both qualitative and quantitative experiments implemented on visible (Vis) and infrared (IR) UAV videos prove that the presented tracker can achieve better performances in terms of precision rate and success rate when compared with other state-of-the-art trackers.