Lightweight Bidirectional Similarity‐Aware Network With Multi‐Scale Features for Remote‐Sensing Mining Area Detection

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Open‐pit mining areas in remote‐sensing imagery present significant challenges for object detection due to complex feature compositions, large scale variations, weak inter‐class discriminability, and sparse spatial distributions. Although single‐stage object detection models provide high computational efficiency and strong real‐time performance, their detection accuracy remains limited when applied to such complex scenarios. To address this issue, this study proposes a lightweight similarity‐aware network with residual multi‐scale bidirectional features, termed LBS‐YOLO, for object detection in open‐pit mining areas. In the neck network, a lightweight residual feature extraction and multi‐scale cross‐kernel perception integration module is introduced to enhance contextual information capture across multiple scales. A bidirectional feature fusion structure is further incorporated in the connection layer to improve multi‐scale feature interaction and better handle objects with diverse spatial scales. In the detection head, a similarity‐aware activation module dynamically adjusts attention weights according to feature statistics, reducing model complexity while enhancing feature representation capability. Experiments conducted on the China's Northern Open‐pit Mining Area dataset and the Brick Kiln dataset demonstrate that LBS‐YOLO achieves mAP 50–95 values of 61.765% and 66.864%, respectively. The results indicate that the proposed method provides accurate and robust detection performance for complex open‐pit mining environments.