Image Recognition System for Mechanical Parts Based on SSD and YOLOv5

• Published in: Journal of Electrical Systems Vol. 20; no. 3; pp. 852 - 869
• Main Authors: Chen, Song, Guo, Dongting, Wang, Dagui, Wang, Fangbin, Tang, Han
• Format: Journal Article
• Language: English
• Published: Paris Engineering and Scientific Research Groups 25.04.2024
• Subjects: Algorithms; Deep learning; Embedded systems; Image enhancement; Image quality; Mechanical components; Object recognition
• ISSN: 1112-5209
• DOI: 10.52783/jes.3016

With the development of industrial automation, the importance of mechanical parts in the production process is becoming increasingly important. In recent years, deep learning technology has made breakthrough progress in fields such as image recognition and object detection. Deep learning models can learn a large amount of data through multiple epochs, automatically extract features from it, and achieve image recognition and object detection. This provides potent technical assistance for the design of mechanical component recognition systems. In industrial production, there are a wide variety of mechanical parts with complex shapes, and the differences between different models are often very small, making it difficult to identify them manually. Therefore, it is necessary to develop a system that can automatically identify mechanical parts to improve production efficiency and quality. This article takes mechanical parts as the research object and studies the recognition technology of mechanical parts based on deep learning and image recognition. The main content of the article includes the following aspects. Research was conducted on image quality enhancement methods for images with different characteristics, and different image preprocessing methods were applied to different images. YOLOv5 and SSD (Single Shot MultiBox Detector) algorithms are used to perform deep learning and recognition detection on the processed images, respectively. By comprehensively comparing the differences between the two algorithms, YOLOv5 was selected as the focus of this design, and based on this, deep training was conducted, and the code was imported into the embedded system.