Lian Yu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Lian Yu has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Fusheng Yu
School of Mathematical Sciences, Key Laboratory of Mathematics and Complex Systems, Ministry of Education, Beijing Normal University, Beijing, China
1 shared publication - Jianping Ge
Ministry of Education Key Laboratory for Biodiversity Science and Engineering, National Forestry and Grassland Administration Key Laboratory for Conservation Ecology of Northeast Tiger and Leopard National Park, Northeast Tiger and Leopard Biodiversity National Observation and Research Station, National Forestry and Grassland Administration Amur Tiger and Amur Leopard Monitoring and Research Center, College of Life Sciences, Beijing Normal University, Beijing 100875, China
1 shared publication - Jo-Ku Cheng
School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China
1 shared publication - Limin Feng
Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Northeast Tiger and Leopard Biodiversity National Observation and Research Station, National Forestry and Grassland Administration Amur Tiger and Amur Leopard Monitoring and Research Center, National Forestry and Grassland Administration Key Laboratory for Conservation Ecology in Northeast Tiger and Leopard National Park, and School of Life Sciences, Beijing Normal University, Beijing 100875, China
1 shared publication - Mengyu Tan
Ministry of Education Key Laboratory for Biodiversity Science and Engineering, National Forestry and Grassland Administration Key Laboratory for Conservation Ecology of Northeast Tiger and Leopard National Park, Northeast Tiger and Leopard Biodiversity National Observation and Research Station, National Forestry and Grassland Administration Amur Tiger and Amur Leopard Monitoring and Research Center, College of Life Sciences, Beijing Normal University, Beijing 100875, China
1 shared publication - Mo Zhou
School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China
1 shared publication
Latest Publications
- Top Cited PapersOpen Access
Animal Detection and Classification from Camera Trap Images Using Different Mainstream Object Detection Architectures
Camera traps are widely used in wildlife surveys and biodiversity monitoring. Depending on its triggering mechanism, a large number of images or videos are sometimes accumulated. Some literature has proposed the application of deep learning techniques to automatically identify wildlife in camera trap imagery, which can significantly reduce manual work and speed up analysis processes. However, there are few studies validating and comparing the applicability of different models for object detection in real field monitoring scenarios. In this study, we firstly constructed a wildlife image dataset of the Northeast Tiger and Leopard National Park (NTLNP dataset). Furthermore, we evaluated the recognition performance of three currently mainstream object detection architectures and compared the performance of training models on day and night data separately versus together. In this experiment, we selected YOLOv5 series models (anchor-based one-stage), Cascade R-CNN under feature extractor HRNet32 (anchor-based two-stage), and FCOS under feature extractors ResNet50 and ResNet101 (anchor-free one-stage). The experimental results showed that performance of the object detection models of the day-night joint training is satisfying. Specifically, the average result of our models was 0.98 mAP (mean average precision) in the animal image detection and 88% accuracy in the animal video classification. One-stage YOLOv5m achieved the best recognition accuracy. With the help of AI technology, ecologists can extract information from masses of imagery potentially quickly and efficiently, saving much time.
Top Cited PapersOpen AccessAnimal Detection and Classification from Camera Trap Images Using Different Mainstream Object Detection Architectures
Camera traps are widely used in wildlife surveys and biodiversity monitoring. Depending on its triggering mechanism, a large number of images or videos are sometimes accumulated. Some literature has proposed the application of deep learning techniques to automatically identify wildlife in camera trap imagery, which can significantly reduce manual work and speed up analysis processes. However, there are few studies validating and comparing the applicability of different models for object detection in real field monitoring scenarios. In this study, we firstly constructed a wildlife image dataset of the Northeast Tiger and Leopard National Park (NTLNP dataset). Furthermore, we evaluated the recognition performance of three currently mainstream object detection architectures and compared the performance of training models on day and night data separately versus together. In this experiment, we selected YOLOv5 series models (anchor-based one-stage), Cascade R-CNN under feature extractor HRNet32 (anchor-based two-stage), and FCOS under feature extractors ResNet50 and ResNet101 (anchor-free one-stage). The experimental results showed that performance of the object detection models of the day-night joint training is satisfying. Specifically, the average result of our models was 0.98 mAP (mean average precision) in the animal image detection and 88% accuracy in the animal video classification. One-stage YOLOv5m achieved the best recognition accuracy. With the help of AI technology, ecologists can extract information from masses of imagery potentially quickly and efficiently, saving much time.
- 1 July 2019
- Published by Elsevier in Expert Systems with Applications
- Vol. 125, 293-304
Clustering of interval-valued time series of unequal length based on improved dynamic time warping
- 1 July 2019
- Published by Elsevier in Expert Systems with Applications
- Vol. 125, 293-304
Clustering of interval-valued time series of unequal length based on improved dynamic time warping