Semi-Supervised Adaptive Object Detection for Efficient PrecisionAgriculture

Sammanfattning: Existing supervised learning-based detectors for precision agriculturehave previously achieved high accuracy in challenging classificationtasks. However, their performance deteriorate when presented with new environmentsdue to variations in observed objects and surrounding environment.Accordingly, it is desired to accelerate a detector’s adaptability when operatingon new environments. Therefore, this thesis proposes an effective methodfor semi-supervised object detection that can adapt detectors to new environmentswith minimal manual labeling effort. Experimental results show thatthe proposed method reduces annotation efforts by more than 400x while attainingsimilar accuracy to supervised learning alternatives.