Research and Application of Coal Gangue Detection Method Based on Improved YOLOv7-Tiny

Coal gangue sorting is crucial for improving coal quality and reducing environmental pollution; however, traditional methods suffer from resource wastage, high cost, and intensive labor demands. To address these challenges, this paper investigates an image recognition-based coal gangue sorting technique and proposes an improved YOLOv7-tiny detection model tailored for edge GPU devices with limited computational power and memory. YOLOv7-tiny is selected as the baseline due to its balanced performance in detection accuracy, architectural maturity, and deployment stability on edge GPUs. Compared to newer lightweight detectors such as YOLOv8-N and YOLOv6-N, YOLOv7-tiny adopts an ELAN-based modular design, which facilitates structural optimization without relying on anchor-free reconstruction or complex post-training strategies, making it particularly suitable for engineering enhancements in real-time industrial sorting under resource constraints. To tackle the limitations in computing and storage, we first introduce an ELAN-PC feature extraction module based on partial convolution and ELAN. Secondly, a GhostCSP module is proposed by integrating cross-stage aggregation and Ghost bottleneck concepts. These modules replace the original ELAN structures in the backbone and neck networks, significantly reducing floating-point operations (FLOPs) and the number of parameters. Furthermore, the SIoU loss function is adopted to replace the original bounding box loss, enhancing detection accuracy. Experimental results demonstrate that compared with the baseline YOLOv7-tiny, the improved model increases mAP0.5 from 86.9% to 88.7% (a gain of 1.8%), reduces FLOPs from 13.2 G to 9.2 G (a decrease of 30%), and cuts parameters from 6.0 M to 4.3 M (a reduction of 28%). In dynamic sorting tests, the model achieves a coal gangue sorting rate of 82.2% with a misclassification rate of 8.1%, indicating promising practical applicability.