Generative Adversarial Networks for Improving Object Detection in Camouflaged Images

Abstract

The effectiveness of object detection largely depends on the availability of large annotated datasets to train the deep network successfully; however, obtaining a large-scale dataset is expensive and remains a challenge. In this work, we explore two different GAN-based approaches for data augmentation of agricultural images in a camouflaged environment. Camouflage is the property of an object which makes it hard to detect because of its similarity to its environment. We leverage paired and unpaired image-to-image translation to create synthetic images based on custom segmentation masks. We evaluate the quality of synthetic images by applying these to the object detection task as additional training samples. The experiments demonstrate that adversarial-based data augmentation significantly improves the accuracy of region-based convolutional neural network for object detection. Our findings show that when evaluated on the testing dataset, data augmentation achieves detection performance improvement of 3.97%. Given the difficulty of object detection task in camouflaged images, the result suggests that combining adversarial-based data augmentation with the original data can theoretically be synergistic in enhancing deep neural network efficiency to address the open problem of detecting objects in camouflaged environments.