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In deep convolutional neural networks, the introduction of attention mechanism can effectively improve the detection accuracy of multi-scale objects, such as SENet and CBAM. But they use the first-order pooling of feature map to compute the channel weights, which leads to the unreasonable weights allocation. Due to the first-order information can not represent the feature distribution, the differences between channels unable to be distinguished in detail. In this work, we propose a novel object detection method combining multi-level feature fusion and region channel attention (ODMC). First, the positive and negative phase information of multi-level features are fused based on CReLU to enhance the semantic of low-level features and location information of high-level ones. Second, as for the channels after feature fusion, the regional information of feature maps is used to optimize the weight allocation, which can help accurately focus on important channels and suppress irrelevant channels. Finally, the objects are classified and located according to the enhanced features. Extensive experiments on PASCAL VOC and MS COCO benchmark show that ODMC achieves significant improvements over the comparable state-of-the-art detection models with high efficiency.

An Object Detection Method Combining Multi-Level Feature Fusion and Region Channel Attention