Boundary Objectness Network for Object Detection and Localization

In this paper, we present the boundary objectness network (BON), an effective convolutional neural network (CNN) for object detection. Its core contribution is to accurately localize the objects. Generally, the CNN-based localizers predict four bounding box coordinates by learning a regression function. This method shows a low Intersection-of-Union (IoU) with the ground truth box. In our work, the localization is formu-lated as a probabilistic problem. Specifically, the deep features inside the candidate proposal are mapped into a row and a column feature vector, which are called boundary object-ness. The boundary objectness indicates the existence of an object in the horizontal and vertical direction of the proposal, enabling us to elaborately localize the object. Moreover, the modules of object detection share the common convolution-al layers. Meanwhile, a multi-task loss function is designed for joint training strategy. Experimental results on the PAS-CAL VOC datasets demonstrate the competitive performance of our method. For the VGG16 model, we achieve 77.6 % mAP at a speed of 4 frame per second (FPS), thus having the potential for real-time processing.