| Title |
Goral Detection System using YOLOv4 Object Detection Algorithm |
| Authors |
이한성(Han-sung Lee) ; 오유정(Yu-jeong Oh) ; 박영철(Yung-chul Park) ; 임상진(Sang Jin Lim) ; 조현종(Hyun-chong Cho) |
| DOI |
https://doi.org/10.5370/KIEE.2022.71.9.1308 |
| Keywords |
Camera trap; Deep learning; Goral; Object detection |
| Abstract |
Global industrialization has made human life comfortable, while causing environmental pollution. Environmental pollution has destroyed wildlife habitats, and as a result, several animals are endangered. In particular, long-tailed goral (Naemorhaedus caudatus) is an international endangered species that must be restored. A huge amount of photographic data has been accumulated through camera trap research for the restoration of endangered species. Until now, analysis of such photo data has only relied on experts, so it took a lot of time to analyze camera trap data. The goral detection system developed in this study, based on goral camera trap data, can enable goral data analysis at a faster than conventional methods. YOLOv4 was used as an object detection algorithm for goral detection. YOLOv4 is a network based on CSPDarknet53 and has improved performance through Bos (Bag of Specials) and BoF (Bag of Freebies). As a result, goral detection was possible with high accuracy and high speed. The performance of the goral detection system can be decreased by luminance at night. In this work, we propose a light invariant goral detection system by appropriately distributing day and night datasets for training YOLOv4. Test results on the night dataset showed an mAP of 0.907 and test results on the day dataset showed an mAP of 0.927. As a result, the goral detection system showed robust detection performance at night. |