Matching Multi-Scale Features and Prediction Tasks for Real-Time Object Detection

In object detection algorithms based on convolutional neural networks, high-resolution features from lower levels contain more detailed information, which can help the abstract features complete the accurate positioning task; deep-level features contain abstract semantic information, which is more suitable for target existence prediction task. When the most existing anchor-free detection method directly predicts all tasks on the same feature map, it does not match the above features and prediction tasks, which limits the detection accuracy. To this end, the MFT detector, a real-time object detection algorithm, is proposed to match multi-scale features and prediction tasks of targets. MFT detector is based on CenterNet detector, which can match shallow detail features with accurate positioning task, and match multi-scale, multi receptive field abstract features with target existence prediction task. Experimental results show that the proposed MFT detector alleviates the mismatch between features and prediction tasks, and significantly improves the detection precision while maintaining a high speed of 94.5 frame/s, which meets the requirement of a real-time vision system.