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Numerous deep learning-based object detection methods have achieved excellent performance. However, the performance on small-size object detection and positive and negative sample imbalance problems is not satisfactory. We propose a multi-scale feature selective matching network (MFSMNet) to improve the performance of small-size object detection and alleviate the positive and negative sample imbalance problems. First, we construct a multi-scale semantic enhancement module (MSEM) to compensate for the information loss of small-sized targets during down-sampling by obtaining richer semantic information from features at multiple scales. Then, we design the anchor selective matching (ASM) strategy to alleviate the training dominated by negative samples caused by the imbalance of positive and negative samples, which converts the offset values of the localization branch output in the detection head into localization scores and reduces negative samples by discarding low-quality anchors. Finally, a series of quantitative and qualitative experiments on the Microsoft COCO 2017 and PASCAL VOC 2007 + 2012 datasets show that our method is competitive compared to nine other representative methods. MFSMNet runs on a GeForce RTX 3090. [ABSTRACT FROM AUTHOR]

With the development of image segmentation technology, image context information plays an increasingly important role in semantic segmentation. However, due to the complexity of context information in different feature maps, simple context capture operations can easily cause context information omission. Rich context information can better classify categories and improve the quality of image segmentation. On the contrary, poor context information will lead to blurred image category segmentation and an incomplete target edge. In order to capture rich context information as completely as possible, we constructed a Multi-Pooling Context Network (MPCNet), which is a multi-pool contextual network for the semantic segmentation of images. Specifically, we first proposed the Pooling Context Aggregation Module to capture the deep context information of the image by processing the information between the space, channel, and pixel of the image. At the same time, the Spatial Context Module was constructed to capture the detailed spatial context of images at different stages of the network. The whole network structure adopted the form of codec to better extract image context. Finally, we performed extensive experiments on three semantic segmentation datasets (Cityscapes, ADE20K, and PASCAL VOC2012 datasets), which fully proved that our proposed network effectively alleviated the lack of context extraction and verified the effectiveness of the network. [ABSTRACT FROM AUTHOR]