SSDLiteX: Enhancing SSDLite for Small Object Detection
-
Published:2023-11-03
Issue:21
Volume:13
Page:12001
-
ISSN:2076-3417
-
Container-title:Applied Sciences
-
language:en
-
Short-container-title:Applied Sciences
Affiliation:
1. School of Computer Science and Engineering, Korea University of Technology and Education, Cheonan 31253, Republic of Korea
Abstract
Object detection in many real applications requires the capability of detecting small objects in a system with limited resources. Convolutional neural networks (CNNs) show high performance in object detection, but they are not adequate to resource-limited environments. The combination of MobileNet V2 and SSDLite is one of the common choices in such environments, but it has a problem in detecting small objects. This paper analyzes the structure of SSDLite and proposes variations leading to small object detection improvement. The feature maps with the higher resolution are utilized more, and the base CNN is modified to have more layers in the high resolution. Experiments have been performed for the various configurations and the results show the proposed CNN, SSDLiteX, improves the detection accuracy AP of small objects by 1.5 percent points in the MS COCO data set.
Funder
National Research Foundation of Korea Ministry of Education
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference28 articles.
1. ImageNet classification with deep convolutional neural networks;Krizhevsky;Adv. Neural Inf. Process. Syst.,2012 2. Simonyan, K., and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. arXiv. 3. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7–12). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA. 4. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA. 5. Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50× fewer parameters and <0.5 MB model size. arXiv.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
|
|