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DM-YOLOX aerial object detection method with intensive attention mechanism

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Abstract

In aerial image detection, difficulties in feature extraction and low detection accuracy arise due to background interference, occlusion, and the presence of multiple small objects. This paper proposes a DM-YOLOX aerial object target detection method with intensive attention mechanism. Firstly, the proposed approach incorporates coordinate attention (CA) and a dense connection method into the backbone network architecture, enabling adaptive channel weighting throughout the feature extraction process. This facilitates the enhancement of significant features while suppressing less relevant ones, thereby augmenting the network’s capacity to represent object features and ensuring retention and reinforcement of key features. Secondly, the multibranch extraction module (MBE) is incorporated into the feature fusion network to enhance the network’s ability in extracting multi-scale feature information from images with extensive coverage, thereby enhancing the detection accuracy and efficiency of small- and medium-sized objects in complex scenes. Finally, the utilization of SIoU instead of IoU as the bounding box loss function effectively addresses the issue of mismatch between real and predicted boxes, leading to accelerated network convergence and improved performance during model training. After training and testing on the VisDrone 2019 dataset, this method effectively detects small objects in complex environments. The DM-YOLOX model shows a significant improvement of 2.7% in mAP compared to the baseline network, while achieving an 8% increase in frames per second (FPS).

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Data availability

The VisDrone dataset used in this paper are open, which can be downloaded from the Internet.

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Funding

Youth Program of Shaanxi Province: 2022JQ-624. China University Industry Research and Innovation Fund: 2021ALA02002. The Higher Education Teaching Reform Research Project of China Textile Industry Association: 2021BKJGLX004. The Higher Education Research Project of Xi’an Polytechnic University: 20GJ05.

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Authors and Affiliations

  1. Xi’an Polytechnic University, Xi’an, 710600, Shaanxi, China

    Xiangyu Li, Fengping Wang, Wei Wang, Yanjiang Han & Jianyang Zhang

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  1. Xiangyu Li
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  2. Fengping Wang
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  3. Wei Wang
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  4. Yanjiang Han
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  5. Jianyang Zhang
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Contributions

Conceptualization was performed by XL and FW; methodology was done by XL and WW; software was developed by XL; validation was provided by FW; investigation was conducted by XL, YH, and JZ; writing—original draft preparation was revised by XL and FW; writing—review and editing were prpeared by WW and FW; visualization was presented by YH and JZ. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Fengping Wang.

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Li, X., Wang, F., Wang, W. et al. DM-YOLOX aerial object detection method with intensive attention mechanism. J Supercomput (2024). https://doi.org/10.1007/s11227-024-05944-x

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