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<p>Object detection in dense scenes faces challenges due to high overlap, severe occlusion, and complex backgrounds, which lead to feature confusion. Traditional detectors struggle to achieve stable and discriminative multi-scale representations, resulting in a significant drop in accuracy. To address this scientific issue, a novel architecture optimized for dense scenes was proposed - ELM-YOLO (EMA-LSKA-MSDA-YOLO). This method alleviated the aforementioned problems through three intuitive design innovations: The enhanced C2f-EMA (Faster Implementation of CSP Bottleneck with 2 convolutions - Efficient Multi-Scale Attention) module strengthened key semantics and suppressed background interference using multi-scale efficient attention. The SPPF-LSKA (Spatial Pyramid Pooling Fast - Large Separable Kernel Attention) module expanded the receptive field explicitly with large separable convolution kernels to capture long-range associative features of occluded objects. The MSDA (Multi-Scale Dilated Attention) module achieved more comprehensive multi-scale information fusion with an additional detection head, enabling the model to maintain higher discrimination in dense regions with small objects. Experiments based on a self-built dense scene dataset showed that ELM-YOLO achieved significant improvements over YOLOv8s in accuracy, recall, and mAP@0.5, verifying its effectiveness and applicability in densely occluded environments. This code is available at <a href="https://github.com/yunianan866/ELM-YOLO/tree/main">https://github.com/yunianan866/ELM-YOLO/tree/main</a>.</p>

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Enhanced object detection in dense scenes via ELM-YOLO: a multi-scale feature extraction approach

  • 期刊论文

  • 发表日期：

    2026年4月24日

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  • Jinshun Dong,
  • Lixia Deng,
  • Dapeng Wan,
  • Jianqin Yin,
  • Haiying Liu

摘要

Object detection in dense scenes faces challenges due to high overlap, severe occlusion, and complex backgrounds, which lead to feature confusion. Traditional detectors struggle to achieve stable and discriminative multi-scale representations, resulting in a significant drop in accuracy. To address this scientific issue, a novel architecture optimized for dense scenes was proposed - ELM-YOLO (EMA-LSKA-MSDA-YOLO). This method alleviated the aforementioned problems through three intuitive design innovations: The enhanced C2f-EMA (Faster Implementation of CSP Bottleneck with 2 convolutions - Efficient Multi-Scale Attention) module strengthened key semantics and suppressed background interference using multi-scale efficient attention. The SPPF-LSKA (Spatial Pyramid Pooling Fast - Large Separable Kernel Attention) module expanded the receptive field explicitly with large separable convolution kernels to capture long-range associative features of occluded objects. The MSDA (Multi-Scale Dilated Attention) module achieved more comprehensive multi-scale information fusion with an additional detection head, enabling the model to maintain higher discrimination in dense regions with small objects. Experiments based on a self-built dense scene dataset showed that ELM-YOLO achieved significant improvements over YOLOv8s in accuracy, recall, and mAP@0.5, verifying its effectiveness and applicability in densely occluded environments. This code is available at https://github.com/yunianan866/ELM-YOLO/tree/main.