Mixed Stage Partial Network and Background Data Augmentation for Surveillance Object Detection

State-of-the-art (SoTA) object detection models and their accuracy have been improved by a large margin via CNNs (Convolutional Neural Networks); however, these models still perform poorly for small road objects. Moreover, the SoTA models are mainly trained on public benchmark datasets such as MS COCO, which include more complicated backgrounds and thus make them robust for object detection. However, for surveillance or road videos, their monotone backgrounds make these SoTA detectors background-over-fitted. In applications such as autonomous driving or traffic flow estimation, the background-over-fitting problem will increase various challenges and lead to accuracy degradation in object detection. One novelty of this paper is to propose an MBA (Mixed Background Augmentation) method to improve detection accuracy without adding new labeling efforts and any pre-training processes. During the inference stage, only one input image is needed for vehicle detection without involving background subtraction. Another novelty of this paper is the design of an efficient MSP (Mixed Stage Partial) network to detect objects more accurately and efficiently from surveillance videos. Extensive experiments on KITTI and UA-DETRAC benchmarks show that the proposed method achieves the SoTA results for highly accurate and efficient vehicle detection. The detection accuracy is improved from 78.53% to 83.59% with 25.7 $fps$ on the UA-DETRAC data set. The implementation code is available at https://github.com/pingyang1117/MSPNet.