Abstract
Wildlife plays a very important role in the ecological balance of the earth. With the rapid development of computer vision, we can use object detection techniques to count the number of wildlife for their better conservation; however, some wildlife live mainly in groups, so the datasets are often distributed in a dense state. It is difficult in dense environments since boxes for different objects should be preserved and duplicate detections should be suppressed at the same time. It is challenging to detect dense wildlife. To solve the problem, we propose an improved model based on YOLOv5. Firstly, we add a Convolutional Block Attention Model(CBAM) to enable the network to extract the richer wildlife features. Secondly, Distance Intersection over Union-Non Maximum Suppression(DIoU-NMS) is used to solve the problem of multiple-objects overlap in wildlife detection to reduce the redundancy of the wildlife. Finally, Efficient Intersection over Union Loss(EIoU Loss) is used to speed up the convergence of the loss function. The experimental results show that our model achieved good performance on the public dataset Wildlife. Compared with the basic YOLOv5s, it reaches 94.2% in mAP@0.5 and achieves average increments by 2.1% . It improves the application and practicability of object detection technology in dense wildlife detection, which is encouraging and meaningful.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kays, R., Tilak, S., Kranstauber, B., Jansen, P.A., Carbone, C., Rowcliffe, M.J., Fountain, T., Eggert, J., He, Z.: Monitoring wild animal communities with arrays of motion sensitive camera traps. arXiv preprint arXiv:1009.5718 (2010)
Jocher, G., Stoken, A., Borovec, J., NanoCode, Chaurasia, A., TaoXie, Changyu, L., Abhiram, Laughing, tkianai, yxNONG, Hogan, A., lorenzomammana, AlexWang, Hajek, J., Diaconu, L., Marc, Kwon, Y., oleg, wanghaoyang, Defretin, Y., Lohia, A., ml ah, Milanko, B., Fineran, B., Khromov, D., Yiwei, D., Doug, Durgesh, Ingham, F.: ultralytics/yolov5: v5.0 - yolov5-p6 1280 models, aws, supervise.ly and youtube integrations (2021)
Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV). pp. 3–19 (2018)
Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D.: Distance-iou loss: Faster and better learning for bounding box regression. In: Proceedings of the AAAI conference on artificial intelligence. vol. 34, pp. 12993–13000 (2020)
Neubeck, A., Van Gool, L.: Efficient non-maximum suppression. In: 18th International Conference on Pattern Recognition (ICPR’06). vol. 3, pp. 850–855. IEEE (2006)
Zhang, Y.F., Ren, W., Zhang, Z., Jia, Z., Wang, L., Tan, T.: Focal and efficient iou loss for accurate bounding box regression. Neurocomputing (2022)
Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 658–666 (2019)
Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning. pp. 6105–6114. PMLR (2019)
Wang, C.Y., Liao, H.Y.M., Wu, Y.H., Chen, P.Y., Hsieh, J.W., Yeh, I.H.: Cspnet: A new backbone that can enhance learning capability of cnn. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp. 390–391 (2020)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4700–4708 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2117–2125 (2017)
Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 8759–8768 (2018)
Tan, M., Pang, R., Le, Q.V.: Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10781–10790 (2020)
Liu, S., Huang, D., Wang, Y.: Learning spatial fusion for single-shot object detection. arXiv preprint arXiv:1911.09516 (2019)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision. pp. 2961–2969 (2017)
Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision. pp. 1440–1448 (2015)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Ssd: Single shot multibox detector. In: European conference on computer vision. pp. 21–37. Springer (2016)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 779–788 (2016)
Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7263–7271 (2017)
Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)
Lahiri, M., Tantipathananandh, C., Warungu, R., Rubenstein, D.I., Berger-Wolf, T.Y.: Biometric animal databases from field photographs: identification of individual zebra in the wild. In: Proceedings of the 1st ACM international conference on multimedia retrieval. pp. 1–8 (2011)
Norouzzadeh, M.S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M.S., Packer, C., Clune, J.: Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proceedings of the National Academy of Sciences 115(25), E5716–E5725 (2018)
Kellenberger, B., Volpi, M., Tuia, D.: Fast animal detection in uav images using convolutional neural networks. In: 2017 IEEE international geoscience and remote sensing symposium (IGARSS). pp. 866–869. IEEE (2017)
Zeppelzauer, M.: Automated detection of elephants in wildlife video. EURASIP journal on image and video processing 2013(1), 1–23 (2013) pp. 40–49. IEEE (2017)
Feng, W., Ju, W., Li, A., Bao, W., Zhang, J.: High-efficiency progressive transmission and automatic recognition of wildlife monitoring images with wisns. IEEE Access 7, 161412–161423 (2019)
Hu, P., Ramanan, D.: Finding tiny faces. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 951–959 (2017)
Liang, D., Geng, Q., Sun, H., Zhou, H., Kaneko, S.: Inferred box harmonization and aggregation for degraded face detection in crowds. Multimedia Tools and Applications pp. 1–20 (2022)
Pei, Y.: Improved YOLOv5 for Dense Wildlife Object Detection. https://www.kaggle.com/datasets/biancaferreira/african-wildlife
Acknowledgments
This study was supported by the National Natural Science Foundation of China under Grant 62176217, the Innovation Team Funds of China West Normal University under Grant KCXTD2022-3, and the Fundamental Research Funds of China West Normal University under Grant 19B045.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Pei, Y., Xu, L., Zheng, B. (2022). Improved YOLOv5 for Dense Wildlife Object Detection. In: Deng, W., et al. Biometric Recognition. CCBR 2022. Lecture Notes in Computer Science, vol 13628. Springer, Cham. https://doi.org/10.1007/978-3-031-20233-9_58
Download citation
DOI: https://doi.org/10.1007/978-3-031-20233-9_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20232-2
Online ISBN: 978-3-031-20233-9
eBook Packages: Computer ScienceComputer Science (R0)