# EXOLOX: An Efficient One-Stage Object Detection Network Based on YOLOX
## 1 Introduction
It expands the receptive field of single-level features using inflated convolution, which outperforms DETR in terms of speed and accuracy. The above detectors have rich semantic information but weak positional information. Although the detection performance is satisfied for detecting large objects, it is still unsatisfactory for detecting small objects. Different from the above methods, the multi-scale prediction method is applied as the main structure of our proposed new neck, and the NLNet [36] structure of the segmentation domain is introduced to enhance the representation ability of shallow features. Non-local Neural Network CNNs use convolution kernels of different sizes to extract local features of the feature map. The adjacent regions of the feature map can only interact by stacking consecutive convolutional blocks, which is too inefficient to capture long-range dependencies. NLNet computes the dependency of each feature point with the full map by the self-attention mechanism. Nevertheless, the self-attention mechanism needs to perform dot product operations among matrices, which will consume a lot of memory. Therefore, some works [37; 38; 39; 40] directly or indirectly addressed the problem of excessive computation of the self-attention mechanism. GCNet [39] performed only one dot product operation, which would directly reduce the amount of calculation. ANNNet [40] reduced the size of Key and Value by means of sampling for decreasing the amount of computation in the dot product operation. Unlike some existing methods for self-attention computation, this paper proposes a cheaper operation to lessen the computational burden. Decoupled Head [PERSON] et al. [16] mentioned in YOLOX that the traditional coupled head classification and regression prediction were all obtained by a single convolution, which would bring a conflict between the two tasks. In consequence, the decoupled head is used for classification and regression tasks separately through a parallel structure. It not only improves the accuracy but also makes the convergence faster. This is not the first time that a parallel structure detection head is used in YOLOX. In some previous detection tasks, both FCOS [24] and RetinaNet utilized a detection head similar to the decoupled head. Although the decoupled head performs better than the traditional coupled head,the stacked convolutional blocks on the parallel branches cause a delay. In testing, we also found that if we additionally increased the number of predicted branches of the detection head, the convergence is faster during training. However, this also results in significant memory consumption. Unlike some parallel structure detection heads, this paper proposes a more efficient decoupled head for the task of object detection. The reparameterization from RepVGG [41] is a better detection technique because its layer fusion operation makes better use of the GPU. Currently, YOLOv6 [42], YOLOv7 [43] and PPYOLOE [44] all utilized structural reparameterization techniques to multiply the FPS by using TensorRT [45]. However, it was found that reparameterization imposed a huge training burden, which is not friendly to low-end GPU devices through some experiments. Fortunately, YOLOX has achieved good real-time performance without applying the structure of RepVGG. Meanwhile, YOLOX is also an anchor free detector, which does not need to carefully design a prior box for a specific detection task. Therefore, YOLOX is chosen as the baseline network to evaluate all proposed modules in this paper. To sum up, a novel network is built for object detection without using FPN, named EYOLOX in an encoder-decoder architecture. Firstly, a novel neck is developed by combining the detection and segmentation methods. Secondly, an efficient detection head is designed by using a simple channel assignment strategy and the structure of the double residual branch. In particular, a low-cost operation in self-attention computation is put forward for reducing the computational burden.