Multi-Scale Faster-RCNN Algorithm for Small Object Detection

Normally, small object is the object which only covers a small part of a whole image. Compared with regular object, small object has less information and the training data of small object is difficult to be marked. This leads to the poor performance when directly employing the previous object detection methods for small object detection. Moreover, the detection methods designed for small object are often too complex or not generic. In this paper, we propose a small object detection algorithm named multi-scale Faster-RCNN. According to the characteristics of convolutional neural network, the structure of Faster-RCNN is modified, such that the network can integrate both the low-level and high-level features for multi-scale object detection. Through such a manner, the accuracy of small object detection is improved. Simultaneously, with the goal of solving the problem that training data is difficult to be marked, we use training data crawled from search engine to train the model. Because the distribution of crawled data is different from the real test data’s, training images in which objects have high resolution are transformed by means of down sampling and up sampling. It makes the feature distribution of training images and test images more similar. The experiment results show that the mean average precision (mAP) of proposed approach can be up to 5% higher than the original Faster-RCNN’s in the task of small object detection.