Advances in International Computer Science

A small object recognition method based on adversarial training is proposed to improve the robustness of the model in complex environments. The method uses YOLOv3 as the base framework, freezing the first few layers and updating only the subsequent layers. The model is trained through real images and generated complex images to adapt to both types of data. Training with a detailed Adam optimizer, small learning rate and batch size. Experimental results show that the proposed method has a higher mAP on small object datasets than YOLOv3, and has high accuracy on complex test sets, indicating that adversarial training does enhance the robustness of the model. However, the speed of this method drops to 0.7 times that of YOLOv3 because the counter image is more complex and requires a longer forward propagation time. In conclusion, adversarial training can significantly improve the robustness of small object recognition models, but it also brings the problem of reduced speed and increased dataset dependency. Further improvements to the model and training strategies are needed to minimize speed and data set impact while maintaining robustness. In summary, this study proposes a small object recognition method based on YOLOv3 and adversarial training, which can significantly improve the robustness of the model in complex environments, but it needs further improvement and optimization.

Small object recognition; YOLO; Adversarial training; Robustness