التفاصيل البيبلوغرافية
| العنوان: |
A Lightweight Object Detection Network for Real-Time Detection of Driver Handheld Call on Embedded Devices. |
| المؤلفون: |
Zhao, Zuopeng1,2 (AUTHOR), Zhang, Zhongxin1,2 (AUTHOR), Xu, Xinzheng1,2 (AUTHOR), Xu, Yi1,2 (AUTHOR), Yan, Hualin1,2 (AUTHOR), Zhang, Lan1,2 (AUTHOR) |
| المصدر: |
Computational Intelligence & Neuroscience. 12/15/2020, p1-12. 12p. |
| مصطلحات موضوعية: |
*ALGORITHMS, *IDENTIFICATION, *COST |
| مستخلص: |
It is necessary to improve the performance of the object detection algorithm in resource-constrained embedded devices by lightweight improvement. In order to further improve the recognition accuracy of the algorithm for small target objects, this paper integrates 5 × 5 deep detachable convolution kernel on the basis of MobileNetV2-SSDLite model, extracts features of two special convolutional layers in addition to detecting the target, and designs a new lightweight object detection network—Lightweight Microscopic Detection Network (LMS-DN). The network can be implemented on embedded devices such as NVIDIA Jetson TX2. The experimental results show that LMS-DN only needs fewer parameters and calculation costs to obtain higher identification accuracy and stronger anti-interference than other popular object detection models. [ABSTRACT FROM AUTHOR] |
| قاعدة البيانات: |
Academic Search Index |
Full Text Finder