The anchor-free method based on key point detection has made great progress. However, the anchor-free method is too dependent on using a convolutional network to generate a rough heatmap. This is difficult to detect for objects with a large size variation and dense and overlapping objects. To solve this problem, first, we propose a mask attention mechanism for object detection methods and make full use of the advantages of the attention mechanism to improve the accuracy of network detection heatmap generation. Then, we designed an optimized fire model to reduce the size of the model. The fire model is an extension of grouped convolution. The fire model allows each group of convolutional network features to learn the same feature through purposeful grouping. In this paper, the mask attention mechanism uses object segmentation images to guide the generation of corner heatmaps. Our approach achieved an accuracy of 91.84% and a recall of 89.83% in the Tencent-100 K dataset. Compared with the popular object detection methods, the proposed method has advantages in model size and accuracy.

中文翻译：

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无需遮罩的物体检测，可遮挡面具

基于关键点检测的免锚方法取得了很大的进步。但是，无锚方法过于依赖使用卷积网络来生成粗糙的热图。对于具有较大尺寸变化的对象以及密集且重叠的对象，这很难检测。为了解决这个问题，首先，我们提出了一种针对物体检测方法的掩模关注机制，并充分利用了关注机制的优势，提高了网络检测热图生成的准确性。然后，我们设计了一个优化的火灾模型以减小模型的大小。火模型是分组卷积的扩展。火模型允许每组卷积网络特征通过有目的的分组学习相同的特征。在本文中，遮罩注意机制使用对象分割图像来指导角热图的生成。我们的方法在腾讯100 K数据集中实现了91.84％的准确性和89.83％的召回率。与流行的物体检测方法相比，该方法在模型尺寸和精度上具有优势。