# End-to-End Object Detection with Enhanced Positive Sample Filter
## 1 Introduction  
Object detection is a fundamental computer vision task. Existing mainstream object detectors have achieved excellent performances, while most of them [1; 2; 3; 4; 5] utilize the non-maximum suppression (NMS) operation, which hinders the deployment of an end-to-end detection pipeline. NMS is a post-processing algorithm that eliminates duplicate detection results. Since the mainstream training strategy is to generate multiple positive samples for one Ground-Truth (GT) object, resulting in dense predictions, NMS serves as a necessary component for obtaining sparse predictions during inference. However, NMS is essentially a manually designed algorithm that relies on hyper-parameters, which limit models' generalization capacity. Especially in crowd scenes, close-by bounding boxes with high confidences are likely to be false positives that hurt detection accuracy. To handle this problem, Soft NMS [6], Learnable NMS [7] and other variants [8; 9; 10] are proposed. However, in real-world industrial deployment, it is inconvenient to use any post-processing strategies, and a fully end-to-end detector is more attractive. Recently, many works [11; 12; 13; 14] have made efforts to discard NMS post-processing and establish a fully end-to-end object detection pipeline. One type of methods formulate object detection as a set prediction problem that directly generates sparse predictions, where DETR [13] and Sparse RCNN [14] are the two most representative works. DETR introduces transformer architecture to object detection. Sparse RCNN establishes a thoroughly sparse detection pipeline based on Faster RCNN [1] architecture. However, when considering thefacts of computation costs, training efficiency and deployment, this kind of set prediction method might not be the optimal choice for actual industrial applications. The other type of method [11; 12] finds that one-to-one label assignment is the key factor for solving the NMS-free problem. They design similar one-to-one label assignment strategies to adaptively choose only one positive sample for each GT object and the remaining samples are considered negative during training. Besides the one-to-one label assignment strategy, DeFCN [12] also designs a 3D max filtering (3 DMF) module for positive sample selection and an auxiliary loss to facilitate the training of the classification task. Thus, it shows a better performance than OneNet [11]. However, the input of the 3 DMF module is the feature maps learned by the regression branch, which are highly related and sensitive to the scales of the input instance. When performing 3D max filtering, responses of larger objects will suppress those of relatively smaller ones, and sometimes the noise in responses will affect the actual correct information, resulting in wrong predictions. Additionally, we notice that the core idea of 3 DMF is utilizing features in adjacent levels to improve the discriminability of convolution in local regions. Specifically, for one feature point, the max pooling operator finds the maximum value of its adjacent feature points in a certain range to update its value. However, the maximum value may be not reliable since there exist false positives and some outliers. In this way, some false predictions will have more severely negative effects in a local region. Both DeFCN and OneNet exploit a one-stage detection framework, which provides the chance to eliminate NMS in actual industrial deployment. We try to tackle the above-mentioned problems and make further steps towards better performance. In this paper, we attempt to establish a high-performance end-to-end object detector that can be easily deployed in industrial applications. The proposed detector is built on top of FCOS [3], which is the state-of-the-art one-stage anchor-free detector. Meanwhile, we utilize the one-to-one label assignment strategy for enabling the training of one-to-one classification.