Improving small object detection with DETRAug

Evair Cunha, David Macêdo, Cleber Zanchettin

Abstract: Small object detection is a challenge for computer vision models due to a shortage of image details, textures, and varying distances from the camera, resulting in objects of different scales and partial occlusion issues. In this paper, we present a new method for enhancing the robustness of image detection models using AUGMIX. Our approach involves applying various augmentations to the input images in a stochastic manner, resulting in a single output image after all transformations have been applied. In addition, we used the Jensen-Shannon loss to maintain a more stable model. In our experiments, we observed a decrease in the number of “no-object” detections, which refers to the detection of unrelated or background objects. The new approach was evaluated using the Deformable DETR, a model known for detecting small objects accurately, and compared to DETR and EfficientDet. We verified an improvement of at least 4.15% using the proposed technique and a more stable loss error.