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<p>Small size, complicated background, and inaccurate target positioning are characteristics of remote sensing targets in the field of target detection. These factors might impair the detection network’s effectiveness and result in misidentification and omission. A module called LSKB_ECA is designed based on the you only look once version 8 (YOLOv8) backbone detection network. It combines the large selective kernel block (LSKB) with the efficient channel attention (ECA) mechanism to dynamically adjust the spatial sensing field and the localization of the key points, enhancing the model’s attention to the target’s features. A module called C2f_C-DCN is designed to replace the cross stage partial feature fusion (C2f) module in YOLOv8 in order to improve the model’s detection ability in complex scenes. The experimental results demonstrate that the enhanced YOLOv8 algorithm boosts the mean average accuracy by 2.7% and 2.1% on the dataset for object detection in aerial images (DOTA) and detection in optical remote sensing images (DIOR) datasets, respectively, signifying a notable enhancement in performance.</p>

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Remote sensing image target detection algorithm based on improved YOLOv8

  • 期刊论文

  • 发表日期：

    2026年7月11日

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  • Chenglong Wang,
  • Tong Liu

摘要

Small size, complicated background, and inaccurate target positioning are characteristics of remote sensing targets in the field of target detection. These factors might impair the detection network’s effectiveness and result in misidentification and omission. A module called LSKB_ECA is designed based on the you only look once version 8 (YOLOv8) backbone detection network. It combines the large selective kernel block (LSKB) with the efficient channel attention (ECA) mechanism to dynamically adjust the spatial sensing field and the localization of the key points, enhancing the model’s attention to the target’s features. A module called C2f_C-DCN is designed to replace the cross stage partial feature fusion (C2f) module in YOLOv8 in order to improve the model’s detection ability in complex scenes. The experimental results demonstrate that the enhanced YOLOv8 algorithm boosts the mean average accuracy by 2.7% and 2.1% on the dataset for object detection in aerial images (DOTA) and detection in optical remote sensing images (DIOR) datasets, respectively, signifying a notable enhancement in performance.