Evaluation of training datasets for typical target detection networks

Deep learning technology has been widely applied in the field of image object detection, and many mature object detection models have emerged, which rely on a large number of data samples for learning and training. However, in many practical application scenarios, it is difficult to obtain a large number of correctly labeled samples. The demand for the quantity and quality of training dataset samples is an important issue in the field of few-shot detection. This paper explores the relationship between sample size and training effectiveness through model training experiments on different datasets. It is found that the accuracy and recall of the model both above 70% when the sample size is more than 500, and less than 10% when the sample size is less than 100. We optimized a dataset of 197 images based on data augmentation, achieving an improvement in training effectiveness by increase 17.1% mean average precision of the model. By adjusting the simulation azimuth and pitch angles to obtain datasets with different sparsity, we trained the detection model using these datasets and tested the model's detection performance using test images. We found that increasing the shooting angle interval would make the dataset sparser, resulting in a decrease in the mean average precision of the model on the validation set and a decrease in the detection performance on the test images. Moreover, an overly sparse dataset could cause over-fitting problems.