结合长尾数据解决方法的野生动物目标检测

基于红外相机图像的野生动物目标检测有利于研究和保护野生动物。由于不同种类的野生动物数量差别大，红外相机采集到的野生动物数据集存在种类数量分布不均的长尾数据问题，进而影响目标检测神经网络模型的整体性能提升。针对野生动物的长尾数据导致的目标检测精度低的问题，提出了一种基于两阶段学习和重加权相结合的长尾数据解决方法，并将该方法用于基于YOLOv4-Tiny的野生动物目标检测。首先，采集、标注并构建了一个新的野生动物数据集，该数据集具有明显的长尾数据特征；其次，采用基于迁移学习的两阶段方法训练神经网络，第一阶段在分类损失函数中采用无加权方式进行训练，而在第二阶段提出了两种改进的重加权方法，并以第一阶段所得权重作为预训练权重进行重加权训练；最后，对野生动物测试集进行测试。实验结果表明，在分类损失采用交叉熵损失函数和焦点损失函数下，所提出的长尾数据解决方法达到了60.47%和61.18%的平均精确率均值（mAP），相较于无加权方法在两种损失函数下分别提高了3.30个百分点和5.16个百分点，相较于所提改进的有效样本加权方法在焦点损失函数下提高了2.14个百分点，说明该方法能提升YOLOv4-Tiny网络对具有长尾数据特征的野生动物数据集的目标检测性能。

Wildlife object detection combined with solving method of long-tail data

Wild animal object detection based on infrared camera images is conducive to the research and protection of wild animals. Because of the large difference in the number of different species of wildlife， there is the long-tail data problem of uneven distribution of numbers of species in the wildlife dataset collected by infrared cameras. This problem affects the overall performance improvement of the object detection neural network models. In order to solve the problem of low accuracy of object detection caused by long-tail data of wild animals， a method based on two-stage learning and re-weighting to solve long-tail data was proposed， and the method was applied to wildlife object detection based on YOLOv4-Tiny. Firstly， a new wildlife dataset with obvious long-tail data characteristics was collected， labelled and constructed. Secondly， a two-stage method based on transfer learning was used to train the neural network. In the first stage， the classification loss function was trained without weighting. In the second stage， two improved re-weighting methods were proposed， and the weights obtained in the first stage were used as the pre-training weights for re-weighting training. Finally， the wildlife test set was used to tested. Experimental results showed that the proposed long-tail data solving method achieved 60.47% and 61.18% mAP （mean Average Precision） with cross-entropy loss function and focal loss function as classification loss respectively， which was 3.30 percentage points and 5.16 percentage points higher than that the no-weighting method under the two loss functions， and 2.14 percentage points higher than that of the proposed improved effective sample weighting method under focus loss function. It shows that the proposed method can improve the object detection performance of YOLOv4-Tiny network for wildlife datasets with long-tail data characteristics.