Instance Segmentation Method Based on Improved Mask R-CNN for the Stacked Electronic Components

Electronics

Hao Xu¹  Ruixia Dong¹  Jinan Gu¹  Zhixian Yang¹ 

[1] School of Mechanical Engineering, Jiangsu University, Zhenjiang 212000, China;

关键词: autonomous detection;    electronic components;    deep learning;    instance segmentation;    Mask R-CNN;   

DOI  :  10.3390/electronics9060886

来源: DOAJ

【 摘 要 】

Object-detection methods based on deep learning play an important role in achieving machine automation. In order to achieve fast and accurate autonomous detection of stacked electronic components, an instance segmentation method based on an improved Mask R-CNN algorithm was proposed. By optimizing the feature extraction network, the performance of Mask R-CNN was improved. A dataset of electronic components containing 1200 images (992 × 744 pixels) was developed, and four types of components were included. Experiments on the dataset showed the model was superior in speed while being more lightweight and more accurate. The speed of our model showed promising results, with twice that of Mask R-CNN. In addition, our model was 0.35 times the size of Mask R-CNN, and the average precision (AP) of our model was improved by about two points compared to Mask R-CNN.

【 授权许可】

Unknown