Book ChapterDOI
Automobile Rim Weld Detection Using the Improved YOLO Algorithm
Zhenxin Yuan,Shuai Zhao,Fu Zhao,Tao Ma,Zhongtao Li +4 more
- pp 122-131
TLDR
Wang et al. as mentioned in this paper proposed an automobile rim weld detection algorithm YOLOv4-head2-BiFPN on the basis of YOLOOv4 algorithm to ensure high accuracy and speed of detection, which does not affect the detection speed by strengthening feature fusion and removing redundant detection heads.Abstract:
At present, the production efficiency of automobile rim in the industrial field is affected by the detection process of automobile rim quality after steel forging. The traditional way is to check welding position manually, which can facilitate the air tightness detection after weld is pressurized. However, this can largely affect production efficiency. By introducing computer vision and image processing, the position of the rim weld can be accurately located, which is more accurate and time-saving. In order to ensure high accuracy and speed of detection, we propose an automobile rim weld detection algorithm YOLOv4-head2-BiFPN on the basis of YOLOv4 algorithm. The experimental results show that, for one thing, it does not affect the detection speed by strengthening feature fusion and removing redundant detection heads. For another, the AP75 of the improved YOLOv4-head2-BiFPN algorithm in the automobile rim weld detection task is 7.7% higher than that of the original YOLOv4 algorithm.read more
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Journal ArticleDOI
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.
Proceedings ArticleDOI
Feature Pyramid Networks for Object Detection
TL;DR: This paper exploits the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost and achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles.
Journal ArticleDOI
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
TL;DR: This work equips the networks with another pooling strategy, "spatial pyramid pooling", to eliminate the above requirement, and develops a new network structure, called SPP-net, which can generate a fixed-length representation regardless of image size/scale.
Proceedings ArticleDOI
EfficientDet: Scalable and Efficient Object Detection
TL;DR: EfficientDetD7 as discussed by the authors proposes a weighted bi-directional feature pyramid network (BiFPN), which allows easy and fast multi-scale feature fusion, and a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time.
Related Papers (5)
Precise initial weld position identification of a fillet weld seam using laser vision technology
Fu-Qiang Liu,Zong-Yi Wang,Yu Ji +2 more