Object Detection Based on Mult-Layer Convolution Feature Fusion and Online Hard Example Mining

Object detection is a significant issue in visual surveillance. Faster region-based convolutional neural network (R-CNN) is a typical object detection algorithm of deep learning; however, neither its generalization ability nor its detection accuracy of small object is high. In this paper, an effective object detection algorithm is proposed for the small and occluded objects, which is based on multi-layer convolution feature fusion (MCI-I-) and online hard example mining (OHEM). First, the candidate regions are generated with region proposal network optimized by MCI-1-. Then, an effective OHEM algorithm is employed to train the region-based ConvNet detector. The hard examples are automatically selected to improve training efficiency. The avoidance of invalid examples accelerates the convergence speed of the model training. The experiments are performed on KITTI data set in intelligent traffic scenario. The proposed method outperforms the popular methods, such as Faster R-CNN, Regionlets, in terms of the overall detection accuracy. Furthermore, our method is good at the detection of small and occluded objects.