# EXOLOX: An Efficient One-Stage Object Detection Network Based on YOLOX
## 1 Introduction  
Object detection is a hot direction of computer vision and digital image processing, which has been widely used in many fields such as robot navigation [1], intelligent video surveillance [2], automatic driving [3; 4; 5], industrial monitoring [6; 7; 8], aerospace and so on. It can reduce the consumption of human capital and has important practical significance. In addition, object detection is also an important prerequisite for many high-level visual processing and analysis tasks, such as behavior analysis [9; 10; 11] and scene semantic understanding [12; 13; 14]. In recent years, with the development of object detection, one-stage object detector has gradually overtaken two-stage object detectors. Since the one-stage detector differs from the two-stage detector in that it does not need to first generate the region of interest through the region proposal network, it has gained popularity due to its balance of speed and accuracy. For example, RetinaNet [15] first surpassed the two-stage object detector as a milestone, and the YOLO series [16; 17; 18; 19; 20; 21] are the most used in the industrial field, etc. The ability of a detector for detecting objects with various sizes is an important indicator to evaluate its performance. In particular, the detection of small objects has always been a challenging task. Thus, in order to solve the problem of poor detection of small objects in SSD [22], [PERSON] et al. [23] proposed FPN to cope with the problem of poor semantic information of the superficial features. After that, many subsequent one-stage object detectors used multi-scale feature fusion with multi-scale prediction to improve the performance [15; 17; 24]. Moreover, some variants of FPN have also been proposed tooptimize the original structure, such as AugFPN [25], NAS-FPN [26], PANet [27], etc. However, two main limitations of FPN have been pointed out by some researchers. One is the problem of computational complexity, and the other one is that many of the features fused by FPN are contradictory information [28]. Therefore, whether good detection can be achieved without using FPN becomes our motivation. Detectors with FPN FPN enriches the semantic information of the low-level features by fusing the high-level features with the low-level features, and the lateral connections maintain multi-scale prediction. Applying FPN both to two-stage object detector (Faster R-CNN [29]) and the one-stage object detector (RetinaNet) has significantly improved the detection performance. Hence, FPN and its variants have made a splash in object detection, and YOLOv3 [17] used FPN to achieve a balance of accuracy and speed that is still popular in industry today. PANet was applied in YOLOv4 [18] and YOLOv5 [19], which added a part of the fusion of low-level features to high-level features on the basis of FPN. EfficientDet [30] proposed BiFPN to iteratively perform bidirectional feature fusion. Unlike the above methods, FPN is not used as an encoder for object detection model in this paper. Detectors without FPN In the early days, Fast-RCNN [31] used features generated by Roipooling layers to make predictions. YOLO [20] and YOLOv2 [21] only employed the last output feature of the backbone for prediction. They are fast, but the deficit of performance is also very obvious compared with applying multi-scale prediction. CenterNet [32] and CornerNet [33] got better results by downsampling four times as many features for prediction. However, huge memory consumption is brought by using high-resolution features. DETR [34] introduced a transformer into object detection and achieved state-of-the-art results by only using the last layer of features. Nevertheless, the long training time of DETR and the difficulty of convergence are inherent disadvantages, and DETR is also powerless for small objects. YOLOF [35] believed that the success of FPN did not contribute to the fusion of multi-scale features but benefited from the divide and conquer.