# GS-AGC: An Adaptive Glare Suppression Algorithm Based on Regional Brightness Perception
## 3 An Adaptive Glare Suppression ALGORITHM Based on Regional Brightness Perception
### Gs-Agc
Moving on to the second row of images, our algorithm and the Zero-DCE algorithm exhibited the same detection accuracy of 0.39 for pedestrians next to the fence. On the other hand, the other algorithms failed to detect these pedestrians in this particular region. Therefore, it offered an effective solution for real-time pedestrian detection on dark roads in embedded systems. For night-time face detection performance evaluation, 500 face images from the training set of the widerface [49] dataset were labelled using a local labelling tool, labelimg, as the coco dataset does not have face detection boxes labelled in its categories. Subsequently, the yolov5\(\times\) model was trained using this labelled portion of the dataset, and experimental testing was conducted using the Dark face [48] dataset. As shown in Figure 10, in the first row of images, only human face detection frames of our augmented images were present. In the second row of images, although the image enhanced by the JinY algorithm had

Figure 9: Comparison of the performance of various algorithms for road pedestrian detection. one more face detection frame than ours, the detection accuracy of this frame was lower, measuring only 0.26, whereas the accuracy of all our other face detection frames was higher compared to that of the JinY algorithm. In the domain of night-time face detection, our augmented image demonstrated the best face detection accuracy when compared to other techniques such as Zero-DCE and histogram equalization algorithms. This underscores that our method is an indispensable pre-processing step in advanced image processing systems. #### 4.3.4 Hardware Energy Consumption and Real-Time Comparison Test

Since embedded systems are more concerned about energy consumption and real-time performance, we conducted comparisons with other general-purpose hardware platform accelerators in terms of energy efficiency and real-time performance. Table 4 shows the test results, the test platforms were Intel Core i7 and Zynq 7020, and the test data were 1280 \(\times\) 720 low-light image files. In terms of energy efficiency, FPGAs make full use of their parallel processing capabilities by rationally designing and configuring hardware circuits to achieve faster runtimes. The PIPELINE instruction was used in the FPGA to demonstrate the parallel processing capability by dividing the Gamma value in Equation (6) into multiple phases and executing them simultaneously, resulting in a faster runtime than on the CPU platform. The FPGA could also be accelerated by customizing the hardware, and the hardware logic was customized according to the requirements of the GS-AGC algorithm. In contrast, CPUs are general-purpose processors that cannot directly optimize the hardware execution of algorithms. Translating the algorithms into hardware circuits can eliminate some of the overheads on the CPU and improve execution efficiency. In terms of real time, FPGAs have low latency, as demonstrated by the latency (1,900,172) and interval (1,900,173) given by the HLS tool. In FPGAs, the circuitry and signal transmission paths are direct, without interference from the operating system and other software layers. As a result, algorithms executed on FPGAs can respond faster to \begin{table}
\begin{tabular}{c c c} \hline \hline
**Platform** & **CPU** & **FPGA** \\ \hline Processor Model & Intel Core i7 & ZYNQ \\ Process Technology (nm) & 10 & 28 \\ Clock Frequency(GHz) & 2.30 & 0.05 \\ Single frame processing time (ms) & 680 & 19 \\ Frames per Second (FPS) & 1.47 & 52 \\ Energy Consumption (W) & 49.50 & 1.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of real time and energy consumption. Figure 10: Comparison of the performance of various algorithms for face detection at night. inputs and outputs, thereby reducing overall execution time.