Multiscale Edge-Enhanced Deep Learning for Cable Connection Visual Inspection of Low-Voltage Switchgear

Abstract

Correct cable connection is critical for safe and reliable operation of the low-voltage switchgear but currently relies on time-consuming and labor-intensive manual inspection. To improve inspection accuracy and efficiency, a novel multiscale edge-enhanced deep learning (MEDL) framework is designed to visually inspect cable connections in a dense cable scenario. Specifically, the MEDL detects the keypoints at the cable–terminal junctions through an encoder–decoder architecture with an edge enhancement (EE) module and a multiscale feature extraction (MSFE) module, followed by a matching stage. The EE module is designed to highlight the edges of the cables, which can, to some extent, suppress environmental interferences. The MSFE module is designed to extract multiscale features at the cable–terminal junctions while guiding the MEDL model to focus on the target regions. In the matching stage, the HDBSCAN is combined with a shared nearest neighbor (SNN) distance metric to cluster candidate keypoints for keypoint matching. The experimental results on cable connection images acquired in real-world scenarios demonstrate the superiority of the MEDL to some existing deep learning methods, achieving a matching accuracy (MA) of 0.9463 at an acceptable inspection speed.