WAID: A Large-Scale Dataset for Wildlife Detection with Drones
Source abstract
Abstract
Drones are widely used for wildlife monitoring. Deep learning algorithms are key to the success of monitoring wildlife with drones, although they face the problem of detecting small targets. To solve this problem, we have introduced the SE-YOLO model, which incorporates a channel self-attention mechanism into the advanced real-time object detection algorithm YOLOv7, enabling the model to perform effectively on small targets. However, there is another barrier; the lack of publicly available UAV wildlife aerial datasets hampers research on UAV wildlife monitoring algorithms. To fill this gap, we present a large-scale, multi-class, high-quality dataset called WAID (Wildlife Aerial Images from Drone), which contains 14,375 UAV aerial images from different environmental conditions, covering six wildlife species and multiple habitat types. We conducted a statistical analysis experiment, an algorithm detection comparison experiment, and a dataset generalization experiment. The statistical analysis experiment demonstrated the dataset characteristics both quantitatively and intuitively. The comparison and generalization experiments compared different types of advanced algorithms as well as the SE-YOLO method from the perspective of the practical application of UAVs for wildlife monitoring. The experimental results show that WAID is suitable for the study of wildlife monitoring algorithms for UAVs, and SE-YOLO is the most effective in this scenario, with a mAP of up to 0.983. This study brings new methods, data, and inspiration to the field of wildlife monitoring by UAVs.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Video Surveillance and Tracking Methods · Advanced Image and Video Retrieval Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 100083, China
Funding
Nation Key R&D Program of China · the Emergency Open Competition Project of National Forestry and Grassland Administration · Outstanding Youth Team Project of Central Universities
References (83)
Show source-provided references
1. Wildlife conservation and management in Mexico10.2193/0091-7648(2006)34[270:WCAMIM]2.0.CO;2 ↗
2. Understanding the diversity of public interests in wildlife conservation10.1111/j.1523-1739.2009.01374.x ↗
3. On the decline of biodiversity due to area loss10.1038/ncomms9837 ↗
4. Animals in dangerous postures enhance learning, but decrease willingness to protect animals10.12973/eurasia.2017.01000a ↗
5. An automatic counter for aerial images of aggregations of large birds10.1080/00063657.2011.588195 ↗
6. Convolutional neural network based automatic object detection on aerial images10.1109/LGRS.2016.2542358 ↗
7. Computer-automated bird detection and counts in high-resolution aerial images: A review10.1111/jofo.12171 ↗
8. Use of unmanned aerial vehicles for livestock monitoring based on streaming K-means clustering10.1016/j.ifacol.2019.12.560 ↗
9. FSSCaps-DetCountNet: Fuzzy soft sets and CapsNet-based detection and counting network for monitoring animals from aerial images10.1117/1.JRS.14.026521 ↗
10. Ward, S., Hensler, J., Alsalam, B., and Gonzalez, L.F. (2016, January 5–12). Autonomous UAVs wildlife detection using thermal imaging, predictive navigation and computer vision. Proceedings of the 2016 IEEE Aerospace Conference, Big Sky, MT, USA.10.1109/AERO.2016.7500671 ↗
11. Barbedo, J.G.A., Koenigkan, L.V., Santos, T.T., and Santos, P.M. (2019). A study on the detection of cattle in UAV images using deep learning. Sensors, 19.10.20944/preprints201912.0089.v1 ↗
12. Automated aerial animal detection when spatial resolution conditions are varied10.1016/j.compag.2022.106689 ↗
13. Hong, S.J., Han, Y., Kim, S.Y., Lee, A.Y., and Kim, G. (2019). Application of deep-learning methods to bird detection using unmanned aerial vehicle imagery. Sensors, 19.10.3390/s19071651 ↗
14. Counting sea lions and elephants from aerial photography using deep learning with density maps10.1186/s40317-021-00247-x ↗
15. Are unmanned aircraft systems (UASs) the future of wildlife monitoring? A review of accomplishments and challenges10.1111/mam.12046 ↗
16. Wang, D., Shao, Q., and Yue, H. (2019). Surveying wild animals from satellites, manned aircraft and unmanned aerial systems (UASs): A review. Remote Sens., 11.10.3390/rs11111308 ↗
17. A survey of transfer learning10.1186/s40537-016-0043-6 ↗
18. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20–25). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.10.1109/CVPR.2009.5206848 ↗
19. Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C.L. (2014, January 6–12). Microsoft Coco: Common objects in context. Proceedings of the Computer Vision—ECCV 2014, 13th European Conference, Zurich, Switzerland.10.1007/978-3-319-10602-1_48 ↗
20. Zheng, X., Kellenberger, B., Gong, R., Hajnsek, I., and Tuia, D. (2021, January 11–17). Self-supervised pretraining and controlled augmentation improve rare wildlife recognition in UAV images. Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, BC, Canada.10.1109/ICCVW54120.2021.00087 ↗
21. Okafor, E., Smit, R., Schomaker, L., and Wiering, M. (2017, January 3–5). Operational data augmentation in classifying single aerial images of animals. Proceedings of the 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), Gdynia, Poland.10.1109/INISTA.2017.8001185 ↗
22. Kellenberger, B., Marcos, D., and Tuia, D. (2018, January 10–14). Best practices to train deep models on imbalanced datasets—A case study on animal detection in aerial imagery. Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Dublin, Ireland.10.1007/978-3-030-10997-4_40 ↗
23. Wang, Y., Han, D., Wang, L., Guo, Y., and Du, H. (2023). Contextualized Small Target Detection Network for Small Target Goat Face Detection. Animals, 13.10.3390/ani13142365 ↗
24. Infrared patch-image model for small target detection in a single image10.1109/TIP.2013.2281420 ↗
25. Chen, C., Liu, M.Y., Tuzel, O., and Xiao, J. (2016, January 20–24). R-CNN for small object detection. Proceedings of the Computer Vision—ACCV 2016, 13th Asian Conference on Computer Vision, Taipei, Taiwan.10.1007/978-3-319-54193-8_14 ↗
26. Zhou, P., Ni, B., Geng, C., Hu, J., and Xu, Y. (2018, January 18–23). Scale-transferrable object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00062 ↗
27. Pham, M.T., Courtrai, L., Friguet, C., Lefèvre, S., and Baussard, A. (2020). YOLO-Fine: One-stage detector of small objects under various backgrounds in remote sensing images. Remote Sens., 12.10.3390/rs12152501 ↗
28. Real-time marine animal detection using YOLO-based deep learning networks in the coral reef ecosystem10.5194/isprs-archives-XLVI-3-W1-2022-301-2022 ↗
29. WilDect-YOLO: An efficient and robust computer vision-based accurate object localization model for automated endangered wildlife detection10.1016/j.ecoinf.2022.101919 ↗
30. Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 18–22). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.10.1109/CVPR52729.2023.00721 ↗
31. He, Y., Su, B., Yan, J., Tang, J., and Liu, C. (2022, January 16–18). Research on underwater object detection of improved YOLOv7 model based on attention mechanism: The underwater detection module YOLOv7-C. Proceedings of the 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence, Dongguan, China.10.1145/3584376.3584432 ↗
32. Attention and performance10.1146/annurev.psych.52.1.629 ↗
33. Small-target predetection with an attention mechanism10.1117/1.1459054 ↗
34. Zuo, Z., Tong, X., Wei, J., Su, S., Wu, P., Guo, R., and Sun, B. (2022). AFFPN: Attention fusion feature pyramid network for small infrared target detection. Remote Sens., 14.10.3390/rs14143412 ↗
35. Lightweight small object detection network with attention mechanism10.37188/OPE.20223008.0998 ↗
36. Kim, M., Jeong, J., and Kim, S. (2021). ECAP-YOLO: Efficient channel attention pyramid YOLO for small object detection in aerial image. Remote Sens., 13.10.3390/rs13234851 ↗
37. Dai, Y., Gieseke, F., Oehmcke, S., Wu, Y., and Barnard, K. (2021, January 3–8). Attentional feature fusion. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.10.1109/WACV48630.2021.00360 ↗
38. Guo, C., Fan, B., Zhang, Q., Xiang, S., and Pan, C. (2020, January 13–19). AugFPN: Improving multi-scale feature learning for object detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.01261 ↗
39. SAR ship detection in complex background based on multi-feature fusion and non-local channel attention mechanism10.1080/01431161.2021.1963003 ↗
40. Song, H., Kim, M., and Lee, J.G. (2019, January 9–15). Selfie: Refurbishing unclean samples for robust deep learning. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA.
41. Ng, X.L., Ong, K.E., Zheng, Q., Ni, Y., Yeo, S.Y., and Liu, J. (2022, January 19–24). Animal Kingdom: A large and diverse dataset for animal behavior understanding. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.10.1109/CVPR52688.2022.01844 ↗
42. Cao, J., Tang, H., Fang, H.S., Shen, X., Lu, C., and Tai, Y.W. (November, January 27). Cross-domain adaptation for animal pose estimation. Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea.
43. Khan, M.H., McDonagh, J., Khan, S., Shahabuddin, M., Arora, A., Khan, F.S., Shao, L., and Tzimiropoulos, G. (2020, January 13–19). Animalweb: A large-scale hierarchical dataset of annotated animal faces. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.00697 ↗
44. Maggiori, E., Tarabalka, Y., Charpiat, G., and Alliez, P. (2017, January 23–28). Can semantic labeling methods generalize to any city? The inria aerial image labeling benchmark. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.10.1109/IGARSS.2017.8127684 ↗
45. Chen, Q., Wang, L., Wu, Y., Wu, G., Guo, Z., and Waslander, S. (2018). Aerial imagery for roof segmentation: A large-scale dataset towards automatic mapping of buildings. arXiv.10.1016/j.isprsjprs.2018.11.011 ↗
46. Van Etten, A., Lindenbaum, D., and Bacastow, T.M. (2018). Spacenet: A remote sensing dataset and challenge series. arXiv.
47. Lee Son, G., Romain, S., Rose, C., Moore, B., Magrane, K., Packer, P., and Wallace, F. (2023). Development of Electronic Monitoring (EM) Computer Vision Systems and Machine Learning Algorithms for Automated Catch Accounting in Alaska Fisheries.
48. Naude, J., and Joubert, D. (2019, January 16–20). The Aerial Elephant Dataset: A New Public Benchmark for Aerial Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.
49. High-quality dataset of protein-bound ligand conformations and its application to benchmarking conformer ensemble generators10.1021/acs.jcim.6b00613 ↗
50. Towards data-driven energy communities: A review of open-source datasets, models and tools10.1016/j.rser.2021.111290 ↗
51. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
52. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11–14). SSD: Single shot multibox detector. Proceedings of the Computer Vision—ECCV 2016, 14th European Conference, Amsterdam, The Netherlands.10.1007/978-3-319-46448-0_2 ↗
53. Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv.
54. Zhang, S., Wen, L., Bian, X., Lei, Z., and Li, S.Z. (2018, January 18–23). Single-shot refinement neural network for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00442 ↗
55. Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23–28). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.10.1109/CVPR.2014.81 ↗
56. Spatial pyramid pooling in deep convolutional networks for visual recognition10.1109/TPAMI.2015.2389824 ↗
57. Girshick, R. (2015, January 7–13). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.10.1109/ICCV.2015.169 ↗
58. Faster R-CNN: Towards real-time object detection with region proposal networks
59. R-FCN: Object detection via region-based fully convolutional networks
60. Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y., and Sun, J. (2017). Light-head R-CNN: In defense of two-stage object detector. arXiv.
61. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27–30). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.308 ↗
62. Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., and Sang, N. (2018, January 8–14). BiSeNet: Bilateral segmentation network for real-time semantic segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01261-8_20 ↗
63. Scene segmentation with dual relation-aware attention network10.1109/TNNLS.2020.3006524 ↗
64. Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13–19). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.01155 ↗
65. Channel-attention-based DenseNet network for remote sensing image scene classification10.1109/JSTARS.2020.3009352 ↗
66. Gated channel attention mechanism YOLOv3 network for small target detection10.1155/2022/8703380 ↗
67. Precision wildlife monitoring using unmanned aerial vehicles10.1038/srep22574 ↗
68. Sirmacek, B., Wegmann, M., Cross, A., Hopcraft, J., Reinartz, P., and Dech, S. (2012, January 1–5). Automatic population counts for improved wildlife management using aerial photography. Proceedings of the 6th International Congress on Environmental Modelling and Software, Leipzig, Germany.
69. Ruwaimana, M., Satyanarayana, B., Otero, V., Muslim, A.M., Syafiq, A.M., Ibrahim, S., Raymaekers, D., Koedam, N., and Dahdouh-Guebas, F. (2018). The advantages of using drones over space-borne imagery in the mapping of mangrove forests. PLoS ONE, 13.10.1371/journal.pone.0200288 ↗
70. Raoult, V., Colefax, A.P., Allan, B.M., Cagnazzi, D., Castelblanco-Martínez, N., Ierodiaconou, D., Johnston, D.W., Landeo-Yauri, S., Lyons, M., and Pirotta, V. (2020). Operational protocols for the use of drones in marine animal research. Drones, 4.10.3390/drones4040064 ↗
71. Mnih, V., and Hinton, G.E. (2010, January 5–11). Learning to detect roads in high-resolution aerial images. Proceedings of the Computer Vision—ECCV 2010, 11th European Conference on Computer Vision, Heraklion, Crete, Greece.10.1007/978-3-642-15567-3_16 ↗
72. Motion based animal detection in aerial videos10.1016/j.procs.2016.07.316 ↗
73. Terrestrial mammalian wildlife responses to Unmanned Aerial Systems approaches10.1038/s41598-019-38610-x ↗
74. Development perspectives for the application of autonomous, unmanned aerial systems (UASs) in wildlife conservation10.1016/j.biocon.2019.108380 ↗
75. YOLOv4_Drone: UAV image target detection based on an improved YOLOv4 algorithm10.1016/j.compeleceng.2021.107261 ↗
76. Benjdira, B., Khursheed, T., Koubaa, A., Ammar, A., and Ouni, K. (2019, January 5–7). Car detection using unmanned aerial vehicles: Comparison between faster R-CNN and YOLOv3. Proceedings of the 2019 1st International Conference on Unmanned Vehicle Systems-Oman (UVS), Muscat, Oman.10.1109/UVS.2019.8658300 ↗
77. Squeeze-and-excitation networks10.1109/TPAMI.2019.2913372 ↗
78. Attention is all you need
79. Visualizing data using t-SNE
80. Xia, G.S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., and Zhang, L. (2018, January 18–23). DOTA: A large-scale dataset for object detection in aerial images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00418 ↗
81. Krichen, M., Adoni, W.Y.H., Mihoub, A., Alzahrani, M.Y., and Nahhal, T. (2022, January 9–11). Security challenges for drone communications: Possible threats, attacks and countermeasures. Proceedings of the 2022 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH), Riyadh, Saudi Arabia.10.1109/SMARTTECH54121.2022.00048 ↗
82. Ko, Y., Kim, J., Duguma, D.G., Astillo, P.V., You, I., and Pau, G. (2021). Drone secure communication protocol for future sensitive applications in military zone. Sensors, 21.10.3390/s21062057 ↗
83. Vergouw, B., Nagel, H., Bondt, G., and Custers, B. (2016). The Future of Drone Use: Opportunities and Threats from Ethical and Legal Perspectives, T.M.C. Asser Press.