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Enhancing Object Detection with Shape-IoU and Scale-Space-Task Collaborative Lightweight Path Aggregation
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DOI：10.3390/app152211976.png)
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Abstract
En 中文
We propose a novel target detection algorithm that addresses the issues of ignoring shape attributes in regression loss and the inability of the high-parameter PAFPN to jointly perceive scale-space-task information. Specifically, we construct a Lightweight Path Aggregation Feature Pyramid Network (LPAFPN) to reduce model parameters by shuffling and fusing features across channels. To further enhance its perception ability, we augment LPAFPN with a scale-space-task joint-perception enhancement module, terming the resulting network ALPAFPN, which can adaptively process joint information of scale, space, and task. Finally, we introduce a shape-scale bounding box regression loss method that focuses on the target's intrinsic attributes to optimize the regression measurement, thereby boosting the detection accuracy. Experimental results show that the proposed algorithm outperforms state-of-the-art algorithms in terms of F1 score, Precision, and Mean Average Precision (mAP) on the PASCAL VOC and VisDrone2019-DET datasets.
Keywords:
object detection
GSConv
PAFPN
joint perception
shape-IoU
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