Abstract
A fast recognition method for assembly line workpieces based on an improved SSD model is proposed to address the problems of low detection accuracy and lack of real-time performance when existing target detection models face small-scale targets and stacked targets. Based on the SSD network, the optimized Inception_Resnet _V2 structure is used to improve its feature extraction layer and enhance the extraction capability of the network for small-scale targets. The repulsion loss (Reploss) is used to optimize the loss function of the SSD network to solve the problem of stacked workpieces. The issue of difficult detection is improved. The robustness of the algorithm is enhanced. The experimental results show that the improved SSD target detection method improves the detection accuracy by 9.69% over the traditional SSD map. The detection speed meets the real-time requirements, which is a better balance of detection real time and accuracy requirements. The algorithm can recognize small-scale and stacked targets with higher category confidence, better algorithm robustness, and better recognition performance compared to the same type of target detection algorithms.
Highlights
With the rapid development of the modern manufacturing industry, the demand for miniaturized, diversified, and personalized workpieces is gradually increasing. e need for production automation and intellectualization by processing enterprises is increasing [1]
Two main methods are currently used: the manual sorting method and the conventional target detection combined with the mechanical arm sorting method [2, 3]. e manual sortition process is heavily impacted by subjective factors and has a relatively high requirement for the operating environment
We enhance the ability to extract detailed and localized information from the SSD network and improve the accuracy of small-scale target detection. e reject loss is used to optimize the loss function of its network and fix the problem that stacked artifacts are difficult to detect. e experiments show that the method has more outstanding performance in terms of precision and speed of detection, which is a good reference value for developing intelligent detection in the manufacturing industry
Summary
With the rapid development of the modern manufacturing industry, the demand for miniaturized, diversified, and personalized workpieces is gradually increasing. e need for production automation and intellectualization by processing enterprises is increasing [1]. Compared with Faster R-CNN, Mask-RCNN, and other similar algorithm models, the regression-based SSD algorithm has the characteristics of solid target feature extraction ability and fast training speed because of the advantages of a variety of algorithms [7, 8]. It is suitable for the real-time detector system. We introduce the Inception_Resnet _V2 module to reduce the sampling of the low-level function map to expand its perceptual field In this way, we enhance the ability to extract detailed and localized information from the SSD network and improve the accuracy of small-scale target detection. We enhance the ability to extract detailed and localized information from the SSD network and improve the accuracy of small-scale target detection. e reject loss is used to optimize the loss function of its network and fix the problem that stacked artifacts are difficult to detect. e experiments show that the method has more outstanding performance in terms of precision and speed of detection, which is a good reference value for developing intelligent detection in the manufacturing industry
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