Polyurethane buffers serve as critical safety protection devices for elevators, with their integrity directly impacting the effectiveness of protective functions during accidents. Current buffer inspections primarily rely on manual patrols, suffering from low inspection frequency, high subjectivity, and significant detection difficulties. To enhance the intelligence and real-time capability of buffer fault detection, this paper proposes a visual fault detection system for elevator buffers based on image enhancement. The system first designs a Hierarchical Fusion Enhancement Module, which effectively suppresses elastic artifacts and significantly enhances crack edge saliency through illumination correction, texture-sensitive guided filtering, and direction-frequency complementary enhancement. It then proposes a gradient-direction texture feature extractor that integrates a gradient-magnitude-weighted Grey-Level Co-occurrence Matrix with a completed local ternary pattern to construct strongly discriminative texture prior features. Finally, a Texture Fusion-Enhanced YOLO detector is developed, which incorporates texture features into the backbone network via a learnable mapping mechanism to achieve early alignment of texture knowledge with depth features. Experimental results indicate that under low-light and complex background conditions, the system achieves a detection accuracy (mAP@0.5) of 0.903 and an F1 Score of 0.891, showing competitive accuracy and robustness within the tested scenarios.
Lai et al. (Mon,) studied this question.