FOLD: Low-Level Image Enhancement for Low-Light Object Detection Based on FPGA MPSoC

Abstract

Object detection has a wide range of applications as the most fundamental and challenging task in computer vision. However, the image quality problems such as low brightness, low contrast, and high noise in low-light scenes cause significant degradation of object detection performance. To address this, this paper focuses on object detection algorithms in low-light scenarios, carries out exploration and research from the aspects of low-light image enhancement and object detection, and proposes low-level image enhancement for low-light object detection based on the FPGA MPSoC method. On the one hand, the low-light dataset is expanded and the YOLOv3 object detection model is trained based on the low-order image enhancement technique, which improves the detection performance of the model in low-light scenarios; on the other hand, the model is deployed on the MPSoC board to achieve an edge object detection system, which improves the detection efficiency. Finally, validation experiments are conducted on the publicly available low-light object detection dataset and the ZU3EG-AXU3EGB MPSoC board, and the results show that the method in this paper can effectively improve the detection accuracy and efficiency.