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Domain-specific data augmentation for on-road object detection based on a deep neural network

This paper proposes a data augmentation strategy for improving on-road object detection based on a deep neural network. The method uses a single camera and detects objects based on an optimized deep neural network for a driving environment. The strategy also uses a single-shot multi-box detector (SSD) for object detection, which is a state-of-the-art deep-learning algorithm. The performance is improved by using data augmentation for an advanced driver assist system (ADAS) specific to on-road object recognition. The problem of object detection is first analyzed based on a deep neural network in the ADAS domain, and then representative object detection methods that use deep neural networks are surveyed. A restricted random crop process is suggested for detecting small objects in an image, and then a patch resampling strategy is proposed for solving the long tail property in an on-road dataset. The proposed ADAS domain-specific data augmentation method is adjusted for the original object detection method based on a deep neural network. The object detection results were evaluated using an embedded board on the KITTI benchmark dataset, and the suggested data augmentation method improves the average precision by 30%.