Bird monitoring intelligence: Integrating Thermal UAV Imagery and Deep Learning Tools


  • Ravindra Nath Tripathi, Aishwarya Ramachandran, Vikas Tripathi, Ruchi Badola, Syed Ainul Hussain


Automatic bird detection, Computer Vision, Deep learning, Image and Video Processing, Thermal imagery, Subtropical wetland.


Birds are excellent indicators of biodiversity and due to their selective association, are ideal for providing insights into the diversity of vegetation, insects, and aquatic life. Bird census, therefore, is an important tool for ecological monitoring. Birds, however, particularly migratory birds, often flock together in large numbers and bird count estimation of such congregations are herculean tasks, subject to large error margins. Computer vision tasks, such as object detection, tracking, and counting, immensely aid environmental monitoring. Despite many innovative techniques, there is still a lot scope for simplifying and improvising the count estimation process, especially through employing technology and artificial intelligences (AI). The integration of AI into drones for on-the-fly problem-solving is an evolving trend. This paper endeavors to offer a comprehensive compilation of potential studies on wildlife using low-altitude UAVs equipped with thermal sensor datasets found in the literature. Further, we tested the ability of a thermal drone to identify and count water birds in a fresh water habitat. The Unmanned Aerial System with an optical and thermal sensor was integrated with widely accepted detection models such as Detection Transformer, Yolo V7 and Yolo V8 to delineate and count the birds. Thermal imagery was found to be excellent in highlighting birds as bright/hot pixels especially against the cooler waterbody. Among the models, DETR achieved the highest precision score of 91.4%, followed closely by YOLOv8 with a precision score of 84.1%. Additionally, DETR exhibited a notable mAP of 89.2%, demonstrating its efficacy in object detection tasks. Interestingly thermal images are also effective in detecting birds even through canopy that otherwise camouflage well in vegetation. The birds didn't show much response to the presence of UAS particularly at late hours of the day. There is a huge scope of applications and research in the field of ecology. Our study illustrates how UAS, thermal imagery, and automated detection algorithms can be combined to efficiently detect and count birds, thereby offering a critical solution towards population count estimation essential for wildlife management.


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How to Cite

Vikas Tripathi, Ruchi Badola, Syed Ainul Hussain, R. N. T. A. R. . (2024). Bird monitoring intelligence: Integrating Thermal UAV Imagery and Deep Learning Tools. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1283–1290. Retrieved from



Research Article