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A platform for research: civil engineering, architecture and urbanism
Real-time weld seam feature extraction in construction sites
Abstract This paper proposes an efficient approach for extracting feature points from weld images in noisy construction environments. Inspired by the human pose estimation, the proposed method reformulates the weld feature point extraction as a skeletal keypoint detection task. A quick object detector locates the weld region amidst complex backgrounds, followed by efficient feature point extraction via two coordinate classification tasks. This approach achieves sub-pixel accuracy at a low computational cost and confines the annotation within one bounding box and four keypoints per image, eliminating pixel-level labeling. Test results demonstrate real-time, accurate feature point extraction with superior efficiency and robustness compared to traditional methods. The proposed approach thus facilitates the quality control for automated welding in real-world construction scenarios.
Highlights A lightweight deep learning-based method is developed for weld seam feature extraction. A novel procedure of weld seam feature extraction inspired by human pose estimation is presented. Weld feature point coordinate location is transformed to horizontal and vertical coordinate classification. The proposed method exhibits better performance compared to the mainstream methods in terms of accuracy and efficiency.
Real-time weld seam feature extraction in construction sites
Abstract This paper proposes an efficient approach for extracting feature points from weld images in noisy construction environments. Inspired by the human pose estimation, the proposed method reformulates the weld feature point extraction as a skeletal keypoint detection task. A quick object detector locates the weld region amidst complex backgrounds, followed by efficient feature point extraction via two coordinate classification tasks. This approach achieves sub-pixel accuracy at a low computational cost and confines the annotation within one bounding box and four keypoints per image, eliminating pixel-level labeling. Test results demonstrate real-time, accurate feature point extraction with superior efficiency and robustness compared to traditional methods. The proposed approach thus facilitates the quality control for automated welding in real-world construction scenarios.
Highlights A lightweight deep learning-based method is developed for weld seam feature extraction. A novel procedure of weld seam feature extraction inspired by human pose estimation is presented. Weld feature point coordinate location is transformed to horizontal and vertical coordinate classification. The proposed method exhibits better performance compared to the mainstream methods in terms of accuracy and efficiency.
Real-time weld seam feature extraction in construction sites
Cheng, Jiaming (author) / Jin, Hui (author) / Qian, Xudong (author)
2024-02-07
Article (Journal)
Electronic Resource
English
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