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YOLO-AA: an efficient object detection model via strengthening fusion context information

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Abstract

Recently, deep learning-based object detection method has achieved remarkable success. Among them, YOLOv4 has attracted increasing attention with its high accuracy and real-time performance. However, the grasp of contextual semantic information is often unsatisfactory, which is mainly caused by the internal details of the network. To address this issue, we propose an efficient model YOLO-AA (YOLO Model Based on Attention and Atrous Spatial Pyramid Pooling) that enhances the fusion of contextual information. First, we mark the object area a certain degree of attention in different nodes of network propagation, so that it will be conducive to pay more attention to instrumental information; Secondly, considering the issue of parameter and computational complexity, the neck region was optimized, so that the improved model can achieve similar or even better results than the original algorithm with fewer parameter quantities; Then, inspired by the semantic segmentation model DeepLabv3 + , we replace the pooling operation in the Spatial Pyramid Pooling (SPP) module by introducing Depth-wise Separable Convolutions with different dilation rates, with the aim of reflecting multi-scale contextual semantic relationships. Experimental results show that our model has fewer parameters (with 22.83% reduction) while producing higher accuracy (9.02% and 16.89% improvement on the two distinct datasets) compared with the original YOLOv4, which is also competitive to some other representative algorithms.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (NSFC) (61976123, 61601427); Taishan Young Scholars Program of Shandong Province; and Key Development Program for Basic Research of Shandong Province (ZR2020ZD44).

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Authors and Affiliations

  1. School of Computer Science and Technology, Ocean University of China, Qingdao, China

    Miao Zhang & Gaige Wang

  2. School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China

    Muwei Jian

  3. School of Information Science and Technology, Linyi University, Linyi, China

    Muwei Jian

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  1. Miao Zhang
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  2. Muwei Jian
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Correspondence to Muwei Jian or Gaige Wang.

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Zhang, M., Jian, M. & Wang, G. YOLO-AA: an efficient object detection model via strengthening fusion context information. Multimed Tools Appl 83, 10661–10676 (2024). https://doi.org/10.1007/s11042-023-16063-9

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  • DOI: https://doi.org/10.1007/s11042-023-16063-9

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