%0 Journal Article %A ZHOU Tianyu %A ZHU Qibing %A HUANG Min %A XU Xiaoxiang %T Defect Detection of Chip on Carrier Based on Lightweight Convolutional Neural Network %D 2022 %R 10.3778/j.issn.1002-8331.2104-0299 %J Computer Engineering and Applications %P 213-219 %V 58 %N 7 %X Chip on carrier(COC) is an important component of transmitter optical subassembly(TOSA) and is widely used in the field of optical communication to realize photoelectric conversion. Aiming at the problem of real-time detection of three different types of defects on COC, such as collapse, positioning column damage and waveguide stain, a defect detection algorithm of COC based on lightweight convolution neural network, YOLO-Efficientnet, is proposed. Firstly, in order to reduce network parameters and shorten detection time, lightweight convolution neural network, efficientnet, is used for image feature extraction as a backbone network. On the basis of MBConv, the attention idea of SENet is introduced, and the attention mechanism is introduced in the channel dimension. Secondly, in order to solve the problem of information loss during the downsampling process, the spatial pyramid pooling(SPP) structure is introduced to increase the receptive field of the image and separate more significant context features. Finally, to solve the problems of multi-scale COC defects and difficult detection of small targets in waveguide region, PANet structure is introduced for multi-scale feature fusion. Results of the experiments show that the algorithm proposed in this paper has an accuracy of 98.5% for COC defect detection, and the detection time reaches 0.42 seconds per picture, meeting the requirements of real-time detection. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2104-0299