Small Objects Detection Method Based on Multi-scale Non-local Attention Network

Abstract

Existing small objects detection methods usually detect small objects by multi-scale feature maps or multi-scale fusion features. However, these methods mainly utilize the spatial information of the feature maps but ignore the interdependencies between channels. This paper proposes a novel small objects detection network, the network uses the non-local channel attention module to integrate the global spatial information of the features in the shallow layer, and then calibrates the information between channels. And from the spatial domain and the channel domain, it obtains the long-distance dependence of features and enhances the contextual semantic information of small targets in the shallow features. In addition, the network enhances the feature extraction capability of the deep features through dense connection structure, obtains rich target information, and improves the accuracy and real-time of the object detection task. The experimental results show that the algorithm has good detection results on PASCAL VOC and MS COCO datasets, and it can effectively improve the detection accuracy of small objects under the premise of ensuring the detection speed.

Keywords

• object detection
• convolutional neural network (cnn)
• non-local attention
• dense connection