In target detection of optical remote sensing images, two main obstacles for aircraft target detection are how to extract the candidates in complex gray-scale-multi background and how to confirm the targets in case the target shapes are deformed, irregular or asymmetric, such as that caused by natural conditions (low signal-to-noise ratio, illumination condition or swaying photographing) and occlusion by surrounding objects (boarding bridge, equipment). To solve these issues, an improved active contours algorithm, namely region-scalable fitting energy based threshold (TRSF), and a corner-convex hull based segmentation algorithm (CCHS) are proposed in this paper. Firstly, the maximal variance between-cluster algorithm (Otsu’s algorithm) and region-scalable fitting energy (RSF) algorithm are combined to solve the difficulty of targets extraction in complex and gray-scale-multi backgrounds. Secondly, based on inherent shapes and prominent corners, aircrafts are divided into five fragments by utilizing convex hulls and Harris corner points. Furthermore, a series of new structure features, which describe the proportion of targets part in the fragment to the whole fragment and the proportion of fragment to the whole hull, are identified to judge whether the targets are true or not. Experimental results show that TRSF algorithm could improve extraction accuracy in complex background, and that it is faster than some traditional active contours algorithms. The CCHS is effective to suppress the detection difficulties caused by the irregular shape.

In this paper, we propose a novel method developed for detecting incomplete ship targets under cloud interference and low-contrast ship targets in thin fog based on superpixel segmentation, and outline its application to optical remote sensing images. The detection of ship targets often requires the target to be complete, and the overall features of the ship are used for detection and recognition. When the ship target is obscured by clouds, or the contrast between the ship target and the sea-clutter background is low, there may be incomplete targets, which reduce the effectiveness of recognition. Here, we propose a new method combining constant false alarm rate (CFAR) and superpixel segmentation with feature points (SFCFAR) to solve the above problems. Our newly developed SFCFAR utilizes superpixel segmentation to divide large scenes into many small regions which include target regions and background regions. In remote sensing images, the target occupies a small proportion of pixels in the entire image. In our method, we use superpixel segmentation to divide remote sensing images into meaningful blocks. The target regions are identified using the characteristics of clusters of ship texture features and the texture differences between the target and background regions. This step not only detects the ship target quickly, but also detects ships with low contrast and under cloud cover. In optical remote sensing, ships at sea under thin clouds are not common in practice, and the sample size generated is relatively small, so this problem is not applicable to deep learning algorithms for training, while the SFCFAR algorithm does not require data training to complete the detection task. Experiments show that the proposed SFCFAR algorithm enhances the detection of obscured ship targets under clouds and low-contrast targets in thin fog, compared with traditional target detection methods and as deep learning algorithms, further complementing existing ship detection methods.

Modern subway platforms are generally equipped with platform screen door systems to enhance safety, but the gap between the platform screen doors and train doors may cause passengers or objects to become trapped, leading to accidents. Addressing the issues of excessive parameter counts and computational complexity in existing foreign object intrusion detection algorithms, as well as false positives and false negatives for small objects, this article introduces a lightweight deep learning model based on YOLOv11n, named GA-YOLOv11. First, a lightweight GhostConv convolution module is introduced into the backbone network to reduce computational resource waste in irrelevant areas, thereby lowering model complexity and computational load. Additionally, the GAM attention mechanism is incorporated into the head network to enhance the model’s ability to distinguish features, enabling precise identification of object location and category, and significantly reducing the probability of false positives and false negatives. Experimental results demonstrate that in comparison to the original YOLOv11n model, the improved model achieves 3.3%, 3.2%, 1.2%, and 3.5% improvements in precision, recall, mAP@0.5, and mAP@0.5: 0.95, respectively. In contrast to the original YOLOv11n model, the number of parameters and GFLOPs were reduced by 18% and 7.9%, respectfully, while maintaining the same model size. The improved model is more lightweight while ensuring real-time performance and accuracy, designed for detecting foreign objects in subway platform gaps.

In hazy environments, image quality is degraded by haze and the degraded photos have reduced visibility, making the less vivid and visually attractive. This paper proposes a method for recovering image information from a single hazy image. The dark channel prior algorithm tends to underestimate the transmission of bright areas. To address this problem, an improved dehazing algorithm is proposed in this paper. Assuming that intensity in a dark channel affected by haze produces the same offset, the expected value of the dark channel of a hazy image is used as an approximation of this offset to correct the transmission. However, this correction may neglect scene difference and affect the clarity of the recovered images. Therefore, a weighted residual map is used to enhance contrast and recover more information. Experimental results demonstrate that our algorithm can effectively lessen color oversaturation and restore images with enhanced details. This algorithm provides a more accurate transmission estimation method that can be used with a weighted residual map to eliminate haze and improve contrast.