Due to the increasingly thin, chip, miniaturized and high-precision electronic components, the samples in many links of the production and testing process of components are scattered and disordered. Meanwhile, the low efficiency of manual testing and the instability of detection accuracy make the intelligent material discharging detection of components extremely urgent. In this paper, intelligent requirements for detection are comprehensively analyzed, and technologies such as machine vision imaging optimization, deep learning feature matching, and tiny sample grasping and placement are deeply studied. An automatic discharging system integrating counting module, defect detection module and automatic discharging module is developed to realize intelligent discharging detection of tiny bulk disordered devices.

With the development of electronic technology, the requirements for electronic components are gradually becoming chip, miniaturization and high precision. In this case, problems such as scattered sample parts and poor detection accuracy and stability in traditional production testing methods seriously affect production quality. Therefore, in-depth study of intelligent material discharge detection has become an important way to solve the problem. In this paper, the needs of intelligent detection are comprehensively analyzed, and the machine vision imaging optimization is deeply studied, feature matching and small sample capture placement technologies are learned. In order to realize intelligent discharge detection of micro scattered equipment, an automatic discharge system is developed, which integrates counting module, defect detection module and automatic discharge module.