An Improved SSD Model for Small Size Work-pieces Recognition in Automatic Production Line,ERICDATA高等教育知識庫
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篇名
An Improved SSD Model for Small Size Work-pieces Recognition in Automatic Production Line
並列篇名
An Improved SSD Model for Small Size Work-pieces Recognition in Automatic Production Line
作者 Xiaoning BoZhiyuan ZhangYipeng Wang
英文摘要

Aiming at the problems of slow recognition speed and low recognition accuracy of arbitrarily placed workpiece by machine vision in traditional automated production lines, a workpiece recognition algorithm based on improved SSD is proposed. Firstly, the improved DarkNet53 is used to replace the backbone network in the original SSD network framework, and the network enhancement is used in the backbone network to solve the defect of small target missed detection. Then, channel attention module and deep semantic feature fusion module are added, in order to improve the recognition ability and detection accuracy of the small target features. Lastly, the loss function was optimized, and the problem caused by sample imbalance was solved by changing the weight distribution of positive and negative samples. In the experiment, image datasets of typical bolts, nuts, and connecting plates were constructed for the network training, the experimental results showed that, the recognition accuracy and speed have been optimized and meet the requirements of automatic work-piece detection in actual production, compared with traditional YOLOv4 and the original SSD algorithm in the work-piece recognition task.

 

起訖頁 215-222
關鍵詞 Deep learningAutomatic production lineWork-piece recognitionSSDFeature fusion
刊名 網際網路技術學刊  
期數 202403 (25:2期)
出版單位 台灣學術網路管理委員會
DOI 10.53106/160792642024032502004   複製DOI
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