Semi-autonomous learning for object detection has attracted more and more attention in recent years, which usually tends to find only one object instance with the highest score in each image. However, this strategy usually highlights the most representative part of the object instead of the whole object, which may lead to the loss of a lot of important information. To solve this problem, a novel end-to-end aggregate-guided semi-autonomous learning residual network is proposed to perform object detection. Firstly, a progressive modified residual network (MRN) is applied to the backbone network to make the detector more sensitive to the boundary features of the object. Then, an aggregate-based region-merging strategy (ARMS) is designed to select high-quality instances by selecting aggregation areas and merging these regions. The ARMS selects the aggregation areas that are highly related to the object through association coefficient, and then evaluates the aggregation areas through a similarity coefficient and fuses them to obtain high-quality object instance areas. Finally, a regression-locating branch is further developed to refine the location of the object, which can be optimized jointly with regional classification. Extensive experiments demonstrate that the proposed method is superior to state-of-the-art methods.

中文翻译：

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基于聚合区域实例细化的远程图像目标检测半自主学习算法

用于物体检测的半自主学习近年来受到越来越多的关注，它通常倾向于在每张图像中只找到一个得分最高的物体实例。但是，这种策略通常会突出对象最具代表性的部分而不是整个对象，这可能会导致丢失很多重要信息。为了解决这个问题，提出了一种新颖的端到端聚合引导的半自主学习残差网络来执行对象检测。首先，在骨干网络上应用渐进式修正残差网络（MRN），使检测器对物体的边界特征更加敏感。然后，设计了一种基于聚合的区域合并策略（ARMS），通过选择聚合区域并合并这些区域来选择高质量的实例。ARMS通过关联系数选择与对象高度相关的聚合区域，然后通过相似系数对聚合区域进行评估并融合得到高质量的对象实例区域。最后，进一步开发回归定位分支来细化对象的位置，可以与区域分类联合优化。大量实验表明，所提出的方法优于最先进的方法。可以与区域分类联合优化。大量实验表明，所提出的方法优于最先进的方法。可以与区域分类联合优化。大量实验表明，所提出的方法优于最先进的方法。