MBN‐YOLOv8: Patch‐Aware Attention and Cross‐Scale Feature Fusion Network for Large Coal Detection in Fully Mechanized Mining Face

ABSTRACT The environment of fully mechanized mining face is complex, and most coal targets are small, dense and prone to overlap or occlude. Existing object detection algorithms are difficult to extract features and has poor generalization ability, which makes it difficult to apply to the scene of fully mechanized mining face. To alleviate these problems, we propose a novel patch‐aware and cross‐scale feature fusion network MBN‐YOLOv8 for large coal detection in fully mechanized mining face, which consists of three main innovations: Bidirectional cross‐scale connections and weighted feature fusion network (BiFPN), Multi‐branch patch‐aware attention module (MPA), and Normalized Wasserstein distance loss function (NWDCIoU). The MPA employs multi‐branch feature extraction, patch awareness and an attention mechanism to capture small‐scale feature details while enhancing large‐scale feature information. The BiFPN utilizes a bidirectional path feature transfer mechanism and a weighted feature fusion strategy to ensure more comprehensive and detailed cross‐layer transfer and fusion of feature information. The NWDCIoU loss function measures the similarity of the derived Gaussian distribution using normalized wasserstein distance, making it easier for the network to detect scale‐insensitive small targets. Experimental results on the DsLMF+ dataset indicate that the proposed MBN‐YOLOv8 achieves state‐of‐the‐art performance, with mAP@0.5 of 0.872 and precision of 0.825, significantly outperforming nine other state‐of‐the‐art baseline models.