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Deep learning-based object detection algorithms play an increasingly important role in many computer vision tasks. Many detection algorithms are trying to increase the depth of networks to improve their feature expression capacity. However, how to better extract the essential features from a fixed number of training samples is often ignored. Thus, we propose CoReSh network, using the prior knowledge of intrinsic features, to address this problem. First, the relevant optical factors which may affect the detection performance are explored, and the intrinsic features are proven to be able to represent the essential information of objects. Second, a new data augmentation is proposed to increase the feature diversities of training samples through intrinsic decomposition. Third, a deep detection neural network incorporating color, reflectance, and shading image's information is designed. To optimize the performance of our network, different learning rate setting tactics are compared. Besides, a contrast experiment on the Pascal VOC dataset was set up to verify it. The result shows that our network is 10% better than ordinary deep learning-based detection networks in precision, and roughly the same as the network loaded with pre-training weights from ILSVRC. It shows that our network has greatly improved the precision of object detection and it is suitable when the training process lacks sufficient training samples.

An Accurate Object Detector With Effective Feature Extraction by Intrinsic Prior Knowledge