Abstract
Motivated by the need to improve model performance in traffic monitoring tasks with limited labeled samples, we propose a straightforward augmentation technique tailored for object detection datasets, specifically designed for stationary camera-based applications. Our approach focuses on placing objects in the same positions as the originals to ensure its effectiveness. By applying in-place augmentation on objects from the same camera input image, we address the challenge of overlapping with original and previously selected objects. Through extensive testing on two traffic monitoring datasets, we illustrate the efficacy of our augmentation strategy in improving model performance, particularly in scenarios with limited labeled samples and imbalanced class distributions. Notably, our method achieves comparable performance to models trained on the entire dataset while utilizing only 8.5 percent of the original data. Moreover, we report significant improvements, with mAP@.5 increasing from 0.4798 to 0.5025, and the mAP@.5:.95 rising from 0.29 to 0.3138 on the FishEye8K dataset. These results highlight the potential of our augmentation approach in enhancing object detection models for traffic monitoring applications.
Abstract (translated)
为了在有限标注样本的情况下提高交通监测任务的模型性能，我们提出了一个专门针对物体检测数据集的简单增强技术，尤其针对静止相机应用。我们的方法专注于将物体放置在原始位置相同的位置，以确保其有效性。通过在同一相机输入图像上的对象进行原地增强，我们解决了与原始和之前选择的对象重叠的挑战。在两个交通监测数据集上进行广泛的测试，我们证明了我们在增强策略上取得优异性能，特别是在有限标注样本和类别分布不均衡的场景中。值得注意的是，我们的方法在只使用原始数据的8.5％的情况下，实现了与整个数据集训练的模型相当的表现。此外，我们报道了显著的改进，其中mAP@.5从0.4798增加到0.5025，mAP@.5:.95从0.29增加到0.3138在FishEye8K数据集上。这些结果突出了我们在增强交通监测应用中的物体检测模型潜力。
URL
https://arxiv.org/abs/2404.11226
