Research on Surface Damage Detection Model of Steel-Cord Conveyor Belt Based on YOLOv7

Steel-cord conveyor belts are critical equipment in mining operations, and surface damage can easily lead to safety incidents. Therefore, achieving efficient and accurate damage detection is of significant importance. This study investigates a conveyor belt damage identification method based on the YOLOv7 object detection framework. First, a dedicated dataset of conveyor belt damage is constructed, annotated, and augmented using geometric and pixel-level transformations to support model training. To meet real-time detection requirements, the following two lightweight models are proposed on the basis of YOLOv7: GSConv-YOLOv7, which reconstructs the backbone network using GSConv to reduce parameter size and computational cost, and MobileViTv3-YOLOv7, which replaces the original backbone with MobileViTv3. Experimental results show that GSConv-YOLOv7 achieves an mAP of 84.6%, while reducing parameters and computation by 17.2% and 16.2%, respectively, and improving detection speed by 16FPSs. To further enhance accuracy, the MPDIoU loss function is adopted in place of CIoU, improving convergence and bounding box regression performance. Building upon this, an LSKNet attention mechanism is integrated, and most convolutional layers are replaced with RFAConv, resulting in the proposed YOLOv7-GPLF model. This model achieves an mAP of 88.1%, with parameter size and computational cost of 33.7 M and 94.5 G, respectively, and an inference speed of 54 FPSs. The model thus delivers significantly improved detection performance while remaining lightweight, enabling fast and accurate identification of conveyor belt damage.