Lightweight Object Detection Method for Constrained Environments.

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. [ABSTRACT FROM AUTHOR]