Considering the complexity of the button surface texture and the variety of buttons and defects, we propose a fast visual method for button surface defect detection, based on convolutional neural network (CNN). CNN has the ability to extract the essential features by training, avoiding designing complex feature operators adapted to different kinds of buttons, textures and defects. Firstly, we obtain the normalized button region and then use HOG-SVM method to identify the front and back side of the button. Finally, a convolutional neural network is developed to recognize the defects. Aiming at detecting the subtle defects, we propose a network structure with multiple feature channels input. To deal with the defects of different scales, we take a strategy of multi-scale image block detection. The experimental results show that our method is valid for a variety of buttons and able to recognize all kinds of defects that have occurred, including dent, crack, stain, hole, wrong paint and uneven. The detection rate exceeds 96%, which is much better than traditional methods based on SVM and methods based on template match. Our method can reach the speed of 5 fps on DSP based smart camera with 600 MHz frequency.
We carry out teaching based on optoelectronic related course group, aiming at junior students majored in Optoelectronic Information Science and Engineering. " Optoelectronic System Course Project " is product-designing-oriented and lasts for a whole semester. It provides a chance for students to experience the whole process of product designing, and improve their abilities to search literature, proof schemes, design and implement their schemes. In teaching process, each project topic is carefully selected and repeatedly refined to guarantee the projects with the knowledge integrity, engineering meanings and enjoyment. Moreover, we set up a top team with professional and experienced teachers, and build up learning community. Meanwhile, the communication between students and teachers as well as the interaction among students are taken seriously in order to improve their team-work ability and communicational skills. Therefore, students are not only able to have a chance to review the knowledge hierarchy of optics, electronics, and computer sciences, but also are able to improve their engineering mindset and innovation consciousness.