Co-Paced Learning Strategy Based on Confidence for Flying Bird Object Detection Model Training
This work addresses a domain-specific problem for surveillance systems by improving object detection of flying birds, but it is incremental as it builds on existing model training strategies.
The paper tackles the challenge of detecting flying birds in surveillance videos, where varying sizes and background similarity make recognition difficult, by proposing a co-paced learning strategy that selects easy samples first and gradually includes harder ones, resulting in significantly improved detection accuracy on two datasets.
The flying bird objects captured by surveillance cameras exhibit varying levels of recognition difficulty due to factors such as their varying sizes or degrees of similarity to the background. To alleviate the negative impact of hard samples on training the Flying Bird Object Detection (FBOD) model for surveillance videos, we propose the Co-Paced Learning strategy Based on Confidence (CPL-BC) and apply it to the training process of the FBOD model. This strategy involves maintaining two models with identical structures but different initial parameter configurations that collaborate with each other to select easy samples for training, where the prediction confidence exceeds a set threshold. As training progresses, the strategy gradually lowers the threshold, thereby gradually enhancing the model's ability to recognize objects, from easier to more hard ones. Prior to applying CPL-BC, we pre-trained the two FBOD models to equip them with the capability to assess the difficulty of flying bird object samples. Experimental results on two different datasets of flying bird objects in surveillance videos demonstrate that, compared to other model learning strategies, CPL-BC significantly improves detection accuracy, thereby verifying the method's effectiveness and advancement.