WeBox:locating small objects from weak edges

In the object detection task,how to better deal with small objects is a great challenge.The detection accuracy of small objects greatly affects the final detection performance.Our propose a detection framework We Box based on weak edges for small object detection in dense scenes,and proposes to train the richer convolutional features(RCF)edges detection network in a weakly supervised way to generate multi-instance proposals.Then through the region proposal network(RPN)network to locate each object in the multi-instance proposals,in order to ensure the effectiveness of the multi-instance proposals,we correspondingly proposed a multi-instance proposals evaluation criterion.Finally,we use faster region-based convolutional neural network(R-CNN)to process We Box single-instance proposals and fine-tune the final results at the pixel level.The experiments have been carried out on BDCI and TT100 K proves that our method maintains high computational efficiency while effectively improving the accuracy of small objects detection.