Abstract: The lightweight design of object detection models plays an important role in environments with limited computing resources and storage space. To further compress the size of the object detection model and improve its detection accuracy, a higher performance lightweight object detection model named Lite-YOLOX is proposed, which improves the structure of the feature pyramid, the structure of the decoupling head, and the loss function based on the YOLOX-Tiny model. Firstly, to further compress the size of the original model, the structure of the feature pyramid and decoupled head are redesigned to make the neck and head parts of the model lighter. Then, to improve the detection accuracy of the model, the EIoU loss function which is more sensitive to the position of the ground truth box is designed to optimize the proposed model. Finally, the validation experiments are performed on the Pascal VOC and safety helmet wearing dataset. The experimental results show that compared with YOLOX-Tiny, Lite-YOLOX reduces the parameters by 40%, the floating point of operations by 37.5%, and the mAP50 increases by 3.2 and 3.1 percentage points. On the NVIDIA Jetson Xavier NX, the frames per second (FPS)  is increased from 51 to 59, and the real-time performance is significantly improved.

Key words: object detection, lightweight, feature fusion, loss function

摘要： 为了进一步降低目标检测模型YOLOX-Tiny的大小并提高检测精度，以便于更好地适用于计算资源和存储空间有限的环境，在特征金字塔的结构、解耦头的结构和损失函数上对其进行改进，形成一种更高性能的轻量级目标检测模型Lite-YOLOX。为进一步压缩原有模型体积，重新设计特征金字塔和解耦头的结构，使模型的Neck和Head部分更轻量化；为提升模型的检测精度，在原有IoU损失函数的基础上进行优化，设计并提出EIoU损失函数，改进后的损失函数对真实框和预测框的位置更加敏感；选取PASCAL VOC和安全帽检测数据集对改进模型进行验证。实验结果表明：Lite-YOLOX与YOLOX-Tiny相比，参数量减少40%，计算量下降37.5%，mAP50提升3.2和3.1个百分点。在NVIDIA Jetson Xavier NX上，每秒传输帧数（FPS）从51增加到59，实时性有了明显的提升。

关键词: 目标检测, 轻量化, 特征融合, 损失函数