## 1 Introduction  
Traditional inspection methods (such as manual inspections and fixed devices) exhibit certain limitations: relying primarily on manual operation, they suffer from issues like low efficiency and high risks. In response to these challenges, the intelligent inspection robot solution has been proposed. Intelligent inspection robots are widely deployed across various domains, including daily life safety [1], robot navigation [2], intelligent video surveillance [3], traffic scene detection [4] and aerospace [5]. To ensure these robots function effectively--for instance, opening substation doors--selecting a suitable object detection algorithm for identifying doors and handles becomes crucial. Within the specific environment of electrical substations, characterized by factors such as poor lighting conditions and excessive distance to detection targets, conventional object detection models may experience missed detections and false detections, thereby compromising detection outcomes. Consequently, enhancing the real-time feature extraction capability of object detection for critical targets under complex environmental conditions is particularly vital. The object detection algorithms for inspection robots are based on deep learning. By training convolutional neural networks (CNNs) [6], they automatically learn feature representations within images. This enables handling complex and variable target morphologies and background environments, demonstrating robust feature representation capabilities and strong generalization abilities. Single-stage detectors such as YOLO [7] and SSD [8] achieve rapid object detection by placing numerous predefined anchor boxes directly on the image and predicting their categories and locations. Two-stage detectors like Faster R-CNN [9] first generate candidate regions via a region proposal network (RPN), and then perform classification and bounding box regression on each candidate region to achieve higher-precision detection. A comparative analysis is presented in Table 1.  
To ensure real-time performance, substation inspection robots employ single-stage detection architectures. In recent years, deep learning-based object detection has advanced significantly in both research and applications. Researchers have implemented extensive improvements upon the YOLO series, including optimizations to feature extraction networks, region of interest (RoI) pooling layers, region proposal networks (RPNs), and non-maximum suppression (NMS) modules. These enhancements have continually elevated detection efficiency and accuracy, driving further development of related technologies. For instance, [PERSON] et al. [10] proposed an Asymptotic Enhancement Network (AENet) integrated with YOLOv3, developing a novel framework named AE-YOLO specifically designed for low-light conditions. To maximize the enhancement network's benefits for downstream detection tasks, AENet adaptively enhances images through pixel-level enhancement and feature-level enhancement, thereby improving detection performance. Separately, [PERSON] et al. [11] introduced a Low-light Image Enhancement Network (LIEN) to adaptively adjust illumination conditions. LIEN can be integrated into YOLOv8 for end-to-end training, eliminating inconsistencies caused by separately trained enhancement modules and instance segmentation algorithms--where improved image quality may not translate to better segmentation results. Moreover, [PERSON] et al. [12] presented 3L-YOLO, a detection method based on YOLOv8n that operates without an image enhancement module. Furthermore, [PERSON] et al. [13] developed LLD-YOLO, an enhanced YOLOv11 for low-light vehicle detection. It incorporates modified DarkNet modules adapted from self-calibrated illumination learning for low-light image enhancement via adaptive illumination adjustment and a C3k2-RA feature extraction enhancement module. Finally, [PERSON] et al. [14] proposed DHSW-YOLO (DH-SENet-WIoU-YOLO), a deep learning model for real-time detection of daily behaviors