通过置信度动态筛选样本，逐步提升鸟类检测模型识别难样本能力。
Co-Paced Learning Strategy Based on Confidence for Flying Bird Object Detection Model Training
- 双模型协作，用置信度筛选易样本训练，逐步降低阈值
- 在两个监控视频数据集上，检测准确率显著优于其他方法
- 适合处理大小不一、背景相似的飞行鸟类检测任务
监控视频中飞行鸟类目标因尺寸差异或与背景相似度高，导致识别难度各异。为缓解难样本对飞行鸟类目标检测（FBOD）模型训练的负面影响，本文提出基于置信度的协同渐进学习策略（CPL-BC），应用于FBOD模型训练。该策略采用结构相同但初始参数不同的两个模型，协作筛选预测置信度高于设定阈值的易样本进行训练；随着训练推进，逐步降低阈值，使模型从识别易样本逐渐过渡到难样本。训练前，先对两个FBOD模型进行预训练，使其具备评估飞行鸟类样本难易程度的能力。在两个不同监控视频数据集上的实验结果表明，相比其他学习策略，CPL-BC显著提升了检测准确率，验证了方法的有效性与先进性。
原文摘要 · Abstract (English)
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.
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