Abstract: As a multi-task learning process, object detection requires better features than classification task. Detectors that predict different scale objects based on multi-scale features have greatly surpassed detectors based on single-scale features. In addition, the feature pyramid structure is used to build advanced semantic feature maps of all scales, thereby further improving the performance of the detector. However, such feature maps do not fully consider the complementary role of contextual information to semantics. Based on the SSD baseline network, a feature fusion method based on residual attention mechanism is used to make full use of the context information. Not only can the high-resolution feature representation capabilities be enhanced by feature fusion, which is more helpful for detecting small-scale objects, but also the attention mechanism is used to strengthen the key features required for prediction. The performance of the model is evaluated on benchmark data set PASCAL VOC, the map of the model with input image sizes of 300 × 300 and 512 × 512 is 78.8% and 80.7%.

Key words: object detection, feature fusion, attention mechanism, multi-scale feature, contextual information