# Automated Wildlife Bird Detection from Drone Footage Using Computer Vision Techniques
## 6 Future Work  
In order to improve the model's overall precision and ensure more acceptable results for this object-detection problem, additional improvements to our dataset are necessary. Figure 15: Graphs displaying the difference between the mAP or IoU of the evaluated results and several confidence threshold values. Graphs generated on 30 May 2023. (**a**) mAP results in various Confidence Thresholds; (**b**) average IoU results in various Confidence Thresholds. More footage with different environments and altitudes needs to be considered, to further increase the complexity and diversity of our dataset. YOLOv4 has demonstrated exceptional performance and has been widely adopted for various computer vision tasks, including object detection. It offers a good balance between accuracy and performance, making it suitable for real-time or near-real-time applications. The decision to use YOLOv4 was based on its proven track record and its ability to provide reliable and efficient bird-detection results. While newer versions of YOLO may offer additional advancements, our research concentrated on YOLOv4 to establish a solid foundation for automatic bird detection. Future work could certainly explore the benefits of newer versions, such as YOLOv7 [34] and YOLOv7-tiny [35], considering their specific improvements, but our current focus was to demonstrate the effectiveness of YOLOv4 and YOLOv4-tiny in the context of bird-detection tasks. This publication is our first milestone in the surveillance of wildlife. As of the time of writing, we are working on the next milestone and are starting to implement and apply tracking techniques using computer vision methods with our trained object detector. While the article may primarily address individual bird detection, it would be valuable to consider incorporating nest counts and nesting success assessments in future research to provide a more comprehensive understanding and surveillance of bird populations [36]. Conceptualization, P.K. ; methodology, P.K., I.T., D.M. and C.S. ; software, D.M. and P.K. ; validation, P.K. and I.T. ; data collection, D.M. ; writing--original draft preparation, D.M., I.T., C.S. and P.K. ; writing--review and editing, D.M. and P.K. ; supervision, P.K., I.T. and C.S. ; project administration, C.S. and P.K. ; funding acquisition, C.S. and P.K. All authors have read and agreed to the published version of the manuscript. This research received funding from the project \"Research and development advanced computational techniques for remote monitoring of wild poultry to prevent the spread of avian influenza using drones and other devices\" submitted in Priority Axis 3 \"Research and Implementation\" of the financing program \"Physical Environment & Innovative actions 2022\" of the Green Fund. Throughout the development of this project, we worked really hard to achieve our goals. Not applicable. Data Availability Statement: Data not available for this study. The authors declare no conflict of interest. References  
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