Abstract
In this paper, we propose an embedded deployment method for low illumination enhancement to address vision-based techniques. Low illumination enhancement of pictures, videos and camera images is achieved by multi-core parallel computation using a Phytium multi-core CPU. Target detection is performed on low illumination enhanced images using YOLO-Fastest network and model inference is accelerated by multi-core parallelism. Low illumination enhancement of camera 1920*1080 images and target detection is implemented with 181% multi-core CPU usage and even distribution of usage across cores. After multi-core acceleration for 1920*1080 video, the inference time per video frame is reduced from 570ms to 300ms, which is about 50% speedup.
| Original language | English |
|---|---|
| Pages (from-to) | 2220-2225 |
| Number of pages | 6 |
| Journal | IET Conference Proceedings |
| Volume | 2023 |
| Issue number | 47 |
| DOIs | |
| Publication status | Published - 2023 |
| Event | IET International Radar Conference 2023, IRC 2023 - Chongqing, China Duration: 3 Dec 2023 → 5 Dec 2023 |
Keywords
- LOW ILLUMINATION ENHANCEMENT
- MULTI-CORE PARALLEL
- PHYTIUM CPU
- TARGET DETECTION
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Han, F., Tang, M., Wei, X., Dong, X., Qi, Q., Lv, S., Liu, Z., Wang, X., & Li, X. (2023). DESIGN AND IMPLEMENTATION OF A MULTI-CORE PARALLEL LOW ILLUMINATION ENHANCED TARGET DETECTION SYSTEM. IET Conference Proceedings, 2023(47), 2220-2225. https://doi.org/10.1049/icp.2024.1431