This study proposes the YOLOBIRDS algorithm to solve the challenges of several model parameters, high amount of calculation, and a considerable imbalance of positive and negative samples in bird detection tasks in natural scenes. The feature extraction network model was modified, and the standard convolution neural network structure was modified to the depthwise separable residual model. Additionally, the loss function was modified, and the object box size and position loss function were modified from mean square error to generalized intersection over union (CIoU). The confidence loss function includes the positive and negative sample control parameters. The experimental results show that in the Hengshui Lake bird dataset, the mean average precision (mAP) of the YOLOBIRDS algorithm reaches 87.12%, which is 2.71 percentage points higher than that of the original algorithm. Moreover, number of parameters reaches 12425917, which is 79.88% lower than that of the original algorithm. Finally, the speed reaches 32.67 frame/s, which is 19.98% higher than that of the original algorithm. The new model trained by the proposed algorithm has higher accuracy and faster detection speed, which greatly improves the overall recognition rate of bird detection and balances the loss weight of positive and negative samples.