C-CNN: Cascaded convolutional neural network for small deformable and low contrast object localization

Traditionally, the normalized cross correlation (NCC) based or shape based template matching methods are utilized in machine vision to locate an object for a robot pick and place or other automatic equipment. For stability, well-designed LED lighting must be mounted to uniform and stabilize lighting condition. Even so, these algorithms are not robust to detect the small, blurred, or large deformed target in industrial environment. In this paper, we propose a convolutional neural network (CNN) based object localization method, called C-CNN: cascaded convolutional neural network, to overcome the disadvantages of the conventional methods. Our C-CNN method first applies a shallow CNN densely scanning the whole image, most of the background regions are rejected by the network. Then two CNNs are adopted to further evaluate the passed windows and the windows around. A relatively deep model net-4 is applied to adjust the passed windows at last and the adjusted windows are regarded as final positions. The experimental results show that our method can achieve real time detection at the rate of 14FPS and be robust with a small size of training data. The detection accuracy is much higher than traditional methods and state-of-the-art methods.