| Popis: |
Images captured in low-light conditions present significant challenges for accurate object detection due to factors such as high noise, poor illumination, and low contrast. In this study, we propose an innovative approach that combines pixel-wise depth refinement and TensorRT optimization to improve object detection performance and inference speed under challenging lighting scenarios. The Pixel-wise depth refinement utilizes a deep learning model to estimate the depth curve for each pixel in the low-light image, providing detailed scene depth information. We used the Lightweight YOLOv8 object detection model using TensorRT. This will reduce the model's computational complexity and memory footprint, enabling faster inference and real-time performance on resource-constrained devices. By combining these techniques, our approach aims to accomplish enhanced entity recognition precision in reduced-illumination environments while maintaining efficiency and real-time performance. We evaluate the effectiveness of our proposed modifications to the depth curve estimation block and the overall approach on benchmark datasets and demonstrate its potential for enhancing object detection capabilities in challenging low-light conditions. Furthermore, the experimental results show an improvement in the precision, recall, and mean average precisions (mAP), and also by using the TensorRT the inference speed is four times faster than the existing one. |
