Investigation of Grayscale Characterization and Enhanced YOLOv8n for Coal and Gangue Detection

To address the decline in detection accuracy caused by the degradation of grayscale features under environmental interference, a lightweight detection model driven by grayscale characterization, YOLOv8n-CoalGangue, is proposed based on an in-depth analysis of the dynamic variations exhibited by grayscale features. First, grayscale histograms are used to quantitatively evaluate the effects of illumination changes and moisture conditions on feature distributions, revealing that global grayscale aliasing and local texture degradation are the key visual feature bottlenecks. Guided by these unique findings, targeted technological innovations are integrated into the developed architecture. HGNetV2-G, which incorporates the GhostNet principle, is used as the backbone to reduce the incurred computational cost while preserving the core feature extraction ability of the model. A mixed local channel attention (MLCA) mechanism is introduced in the neck to filter background noise and focus on local high-frequency features, which helps overcome global grayscale aliasing issues. In addition, a DGFPN-based feature fusion network is constructed by combining RepGFPN and DySample, together with lightweight shared convolution detection (LSCD), which compensates for the loss of multiscale grayscale details without increasing the imposed parameter burden. Furthermore, the PIoUv2 loss function improves the bounding-box regression process in dense overlapping scenarios. Experimental results show that the proposed model achieves an mAP@50 of 97.2% with a 32% reduction in the number of parameters required (only 2.1 M). It also demonstrates strong robustness under six extreme industrial conditions, such as low illumination and coal dust occlusion, confirming the effectiveness of the design driven by grayscale characterization for practical green mining applications.