PEDNet: A Lightweight Detection Network of Power Equipment in Infrared Image Based on YOLOv4-Tiny

As a promising and noncontact detection technique, machine vision has been widely used in fault diagnosis of substation equipment. The rapid and accurate detection of substation equipment in infrared images is one of the key steps for automatic fault diagnosis. However, the complexity of image background, the low contrast of infrared images, and the rotational targets in infrared images pose a great challenge to detection task. This study aims to improve the detection accuracy of the model while having real-time detection speed and propose a lightweight power equipment detection network (PEDNet) based on You Only Look Once (YOLOv4)-tiny. First, a novel global information aggregation module (GIAM) is constructed to guide the network to focus on the salient regions where the target equipment is located. Second, an improved spatial transformer network (ISTN) is introduced to reduce the impact of rotational targets on detection accuracy. Finally, a feature enhanced fusion network (FEFN) is designed through the use of a multiscale feature cross-fusion structure. It can fully fuse the feature information of the salient region, the rotational targets, and the strong semantic information. The experimental results show that the proposed PEDNet can reach 92.66% detection accuracy and 107.07 frames/s real-time detection speed on the testing datasets. Compared with YOLOv4-tiny, there is a small sacrifice in detection speed, but the detection accuracy is improved and significantly higher than the existing state-of-the-art (SOTA) object detection models.