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Changing Background to Foreground: An Augmentation Method Based on Conditional Generative Network for Stingray Detection

Image processing has been a popular tool for biological researches. Detecting specific animals in aerial images captured by an UAV is a crucial research topic. As the rapid progress of deep learning (DL), it has been a popular approach to many image classification and object detection tasks. However, DL usually requires a large set of training samples to learn the network weights, while the biological image materials are often insufficient to fulfill the demand. To improve the detection of stingrays in aerial images, this paper presents a new training sample augmentation method called Mixed Bg-Fg Synthesis. We extend a generative network, Generative Latent Optimization (GLO) to its conditional version, namely, Conditional GLO (C-GLO), which can increase stingray samples on the background and thus improve the training efficacy of a CNN detector. Unlike traditional data augmentation methods that generate new data only for image classification, our proposed method that mixes foreground and background together can generate new data for an object detection task. Experimental results show that the C-GLO augmented stingray samples is helpful to enhance the detection capability.