Abstract

The common detection of fasteners of automobile door panels is based on the method of template matching, which has the problems of low detection accuracy and poor real-time performance under the influence of different lighting and different placement positions. To improve the detection speed and accuracy of fasteners in complex scenes, a small object detection algorithm, YOLO-DTO (Detect Tiny Object), was proposed based on the YOLOv8 algorithm. Firstly, considering that the algorithm uses strided convolution to compress the input image prematurely, resulting in the loss of fine-grained information in the early stage of the image, which makes it difficult to recover the complete detail information in the subsequent feature fusion process, this paper modifies the convolution module in the early stage of the algorithm and introduces the SPD (SPace-to-Depth) module to reconstruct the early stage of the original algorithm. Secondly, a selective attention module is embedded in the Neck output position of the algorithm to enhance the algorithm's ability to pay attention to the context information of fasteners. Finally, to optimize the regression efficiency of the bounding box, the MPDIoU loss function replaced the CIoU loss function. Experimental results show that the average detection accuracy of the YOLO-DTO algorithm is 98.8

Keywords

MSC

68T45

Cite this article

@article{cai-7112,
  author  = {Xiaohui Wang and Yunshuo Jia and Fengjuan Guo},
  title   = {YOLO-DTO: Automotive Door Panel Fastener Detection Algorithm Based on Deep Learning},
  journal = {Computing and Informatics},
  volume  = {44},
  number  = {2},
  pages   = {385--407},
  year    = {2025},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2025_2_385}
}
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