Wildlife Small Object Detection based on Enhanced Network in Ecological Surveillance

Dai, Wan; Wang, Hongpeng; Song, Yulin; Xin, Yunwei

doi:10.1109/ccdc52312.2021.9602124

Cited by 3 publications

(2 citation statements)

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“…The YOLO framework and YBUT show potential for active community development [6,24]. Examples of this are architectures based on YOLOv5 that improve the model's ability to detect minutely small objects in drone imagery [12,25], improved infrared image object detection network, YOLO-FIRI [26], and improved YOLOv5 framework to detect wildlife in dense spatial distribution [17].…”

Section: You-only-look-once-based Uav Technologymentioning

confidence: 99%

“…Which model to choose depends on the choice in hardware and application, but the differences in performance between the models are minor compared to the importance of the quality of the training datasets [27]. Several studies have combined aerial footage analysis with the use of machine learning [4,6,8,12,14,15,25,[32][33][34][35][36][37], and the increased use of drones for wildlife surveys highlights the need for automation when analyzing imagery. This opens up possibilities of combining real-time object detection and tracking with commercial drone technology.…”

Section: Similar Studies and Perspectivesmentioning

confidence: 99%

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Using YOLO Object Detection to Identify Hare and Roe Deer in Thermal Aerial Video Footage—Possible Future Applications in Real-Time Automatic Drone Surveillance and Wildlife Monitoring

Povlsen,

Bruhn,

Durdevic

et al. 2023

Drones

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Wildlife monitoring can be time-consuming and expensive, but the fast-developing technologies of uncrewed aerial vehicles, sensors, and machine learning pave the way for automated monitoring. In this study, we trained YOLOv5 neural networks to detect points of interest, hare (Lepus europaeus), and roe deer (Capreolus capreolus) in thermal aerial footage and proposed a method to manually assess the parameter mean average precision (mAP) compared to the number of actual false positive and false negative detections in a subsample. This showed that a mAP close to 1 for a trained model does not necessarily mean perfect detection and provided a method to gain insights into the parameters affecting the trained models’ precision. Furthermore, we provided a basic, conceptual algorithm for implementing real-time object detection in uncrewed aircraft systems equipped with thermal sensors, high zoom capabilities, and a laser rangefinder. Real-time object detection is becoming an invaluable complementary tool for the monitoring of cryptic and nocturnal animals with the use of thermal sensors.

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