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Reinforcement binding quality detection model based on deep convolutional neural network
Reinforcement binding is an important construction technology in construction and civil engineering. Currently, many construction processes in China still heavily rely on manual operation, with high work intensity, low efficiency, and high risk, making it difficult to achieve large-scale and industrial production. Reinforcement binding is a key component of reinforced concrete structures, which directly affects the continuity, stability, and safety of the structure. Therefore, this article proposes the CSW-YOLOv8s algorithm. Firstly, a Channel Feature Extractor (CFE) is designed, and a C2f-CFE module is constructed to enable the network to focus more on dense and homogeneous small-sized objects, solving the problem of difficult detection of small targets in the quality detection task of steel bar tying. At the same time, a Space Channel Feature Extraction Module (SCFEM) was designed to filter out interference information, enhance small target feature information, improve the problem of small target feature information being submerged during the convolution process, and solve the problem of target occlusion in the quality detection task of steel reinforcement binding. To address the issue of missed detection of individual steel bar binding points, Wise IOU is introduced as the loss function of this algorithm. The above improvements effectively solved the problem of insufficient accuracy in detecting small targets in YOLOv8s, and improved the detection accuracy of the model for steel bar binding points.
Reinforcement binding quality detection model based on deep convolutional neural network
Reinforcement binding is an important construction technology in construction and civil engineering. Currently, many construction processes in China still heavily rely on manual operation, with high work intensity, low efficiency, and high risk, making it difficult to achieve large-scale and industrial production. Reinforcement binding is a key component of reinforced concrete structures, which directly affects the continuity, stability, and safety of the structure. Therefore, this article proposes the CSW-YOLOv8s algorithm. Firstly, a Channel Feature Extractor (CFE) is designed, and a C2f-CFE module is constructed to enable the network to focus more on dense and homogeneous small-sized objects, solving the problem of difficult detection of small targets in the quality detection task of steel bar tying. At the same time, a Space Channel Feature Extraction Module (SCFEM) was designed to filter out interference information, enhance small target feature information, improve the problem of small target feature information being submerged during the convolution process, and solve the problem of target occlusion in the quality detection task of steel reinforcement binding. To address the issue of missed detection of individual steel bar binding points, Wise IOU is introduced as the loss function of this algorithm. The above improvements effectively solved the problem of insufficient accuracy in detecting small targets in YOLOv8s, and improved the detection accuracy of the model for steel bar binding points.
Reinforcement binding quality detection model based on deep convolutional neural network
Na, Jing (editor) / He, Shuping (editor) / Zhang, Luo (author) / Sun, Jingxian (author) / Luo, Shen (author) / Meng, Fei (author) / Zhang, Cheng (author) / Xie, Chaowen (author)
International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024) ; 2024 ; Yinchuan, China
Proc. SPIE ; 13259
2024-09-04
Conference paper
Electronic Resource
English
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