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IoU-aware feature fusion R-CNN for dense object detection

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Abstract

Densely packed scenes contain a multitude of similar or even identical objects positioned closely, which brings about difficulties in object-detecting. In such scenes, people usually embed the feature pyramid network module into Faster R-CNN for multi-scale detection. However, we find it is not necessary. It is observed that there exists a waste of ground-truth boxes during training, causing the imbalance of training samples and the poor performance of Faster R-CNN. Therefore, we propose an online multiple-step sampling method to increase the utilization of ground truth. Besides, we propose a novel IoU-aware feature fusion R-CNN to take place of the feature pyramid network. It could effectively improve the detection accuracy of Faster R-CNN while simplifying the structure of the feature pyramid network. Our approach improves the base detector and the detection results on SKU-110 k benchmarks indicate that our approach offers a good trade-off between accuracy and speed.

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Authors and Affiliations

  1. Information College, Xiamen University, Xiang’an, Xiamen, 361102, Fujian, China

    Jixuan Hong, Xueqin He & Zhaoli Deng

  2. Information College, Xiamen University, Siming, Xiamen, 361005, Fujian, China

    Chenhui Yang

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  1. Jixuan Hong
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  2. Xueqin He
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  4. Chenhui Yang
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Correspondence to Chenhui Yang.

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Hong, J., He, X., Deng, Z. et al. IoU-aware feature fusion R-CNN for dense object detection. Machine Vision and Applications 35, 3 (2024). https://doi.org/10.1007/s00138-023-01483-2

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