Successes and limitations of pretrained YOLO detectors applied to unseen time-lapse images for automated pollinator monitoring DOI
Valentin Ştefan, Thomas Stark, Michael Wurm

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 7, 2025

Abstract Pollinating insects provide essential ecosystem services, and using time-lapse photography to automate their observation could improve monitoring efficiency. Computer vision models, trained on clear citizen science photos, can detect in similar images with high accuracy, but performance taken is unknown. We evaluated the generalisation of three lightweight YOLO detectors (YOLOv5-nano, YOLOv5-small, YOLOv7-tiny), previously images, for detecting ~ 1,300 flower-visiting arthropod individuals nearly 24,000 captured a fixed smartphone setup. These field featured unseen backgrounds smaller arthropods than training data. model highest number trainable parameters, performed best, localising 91.21% Hymenoptera 80.69% Diptera individuals. However, classification recall was lower (80.45% 66.90%, respectively), partly due Syrphidae mimicking challenge smaller, blurrier flower visitors. This study reveals both potential limitations such models real-world automated monitoring, suggesting they work well larger sharply visible pollinators need improvement less sharp cases.

Language: Английский

Successes and limitations of pretrained YOLO detectors applied to unseen time-lapse images for automated pollinator monitoring DOI
Valentin Ştefan, Thomas Stark, Michael Wurm

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 7, 2025

Abstract Pollinating insects provide essential ecosystem services, and using time-lapse photography to automate their observation could improve monitoring efficiency. Computer vision models, trained on clear citizen science photos, can detect in similar images with high accuracy, but performance taken is unknown. We evaluated the generalisation of three lightweight YOLO detectors (YOLOv5-nano, YOLOv5-small, YOLOv7-tiny), previously images, for detecting ~ 1,300 flower-visiting arthropod individuals nearly 24,000 captured a fixed smartphone setup. These field featured unseen backgrounds smaller arthropods than training data. model highest number trainable parameters, performed best, localising 91.21% Hymenoptera 80.69% Diptera individuals. However, classification recall was lower (80.45% 66.90%, respectively), partly due Syrphidae mimicking challenge smaller, blurrier flower visitors. This study reveals both potential limitations such models real-world automated monitoring, suggesting they work well larger sharply visible pollinators need improvement less sharp cases.

Language: Английский

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