# Shadow Hunter: Low-Illumination Object-Detection Algorithm
## 4 Experiment
### Detection Performance  
The ExDark dataset is annotated for object detection, and this paper conducts both training and evaluation on this dataset. The dataset comprises 12 categories, namely Bicycle, Boat, Bottle, Bus, Car, Cat, Chair, Cup, Dog, Motorbike, People, and Table. It is partitioned into the training, validation, and test sets with an 8:1:1 ratio, following the official partitioning criteria. Using the same input size of 640 \(\times\) 640, our proposed algorithm achieves a 1.7% improvement over the baseline, resulting in a detection accuracy of 64.6% and a significant improvement in mAP at 0.5. Table 1 presents a comparison of the proposed algorithm with other popular object-detection algorithms, in terms of accuracy. Table 1 displays a comparison of the proposed algorithm with other currently popular object-detection algorithms. And displayed in Figure 8. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Backbone** & **mAP 0.5\%** \\ \hline YOLOv5s & CSPDarkNet & 62.9 \\ RetinaNet & ResNet50 & 58.1 \\ YOLOv4 & CSPDarkNet & 59.8 \\ EfficientDet-D0 & EfficientNet & 63.1 \\ Faster-RCNN & ResNet50 & 54.3 \\ \hline Ours & CSPDarkNet & 64.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of detection accuracy on the ExDark dataset. Figure 7: This is our brand new header, through our proposed new classification branch and new regression branch, respectively. Additionally, this paper compares the performance of the proposed low-illumination object-detection algorithm with the combination of image-enhancement algorithms and the YOLO series algorithms on the ExDark dataset. The objective is to assess the effectiveness and superiority of the proposed algorithm in addressing the challenges posed by low-illumination conditions for object-detection tasks. In this study, some deep learning-based low-illumination image-enhancement algorithms, including ZeroShot-RUAS [38], Zero-DCE [17], MBLLEN [39], EnlightenGAN [18], were combined with the basic object-detection models. The model weights of the image-enhancement network were retrained and integrated with the YOLOv5s object detector to conduct experiments on low-illumination object detection using the ExDark dataset. Specifically, the test images underwent an initial enhancement process using image-enhancement algorithms before being input into the object detector, thereby obtaining the mean Average Precision (mAP) associated with the respective algorithm (Figure 9). We validate the benefits of the suggested low-illumination object-detection method in terms of detection accuracy through these studies, as shown in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **mAP 0.5\%** & **Processing Speed/Ms** & **Training Time/h** \\ \hline Ours & 64.6 & 54.3 & 7.0 \\ \hline Zero-DCE + Ours & 54.8 & 237.3 & 9.3 \\ \hline ZeroShot-RUAS + Ours & 64.3 & 983.2 & 17.2 \\ \hline Enlighten Gan + Ours & 58.6 & 446.9 & 15.2 \\ \hline MBLLEN + Ours & 63.4 & 993.5 & 18.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of mAP 0.5%, processing speed, and training time among different methods. Figure 8: Comparison of results between various object detection algorithms and our improved algorithm. It is evident from the visualization that our model are both effective and substantial. Upon comparing the results in Table 2, it can be observed that the proposed end-to-end low-illumination object-detection algorithm exhibits superior detection accuracy. On the other hand, a considerable drop in detection accuracy may result from preprocessing low-illumination pictures with deep-learning-based image-enhancement algorithms before feeding them into the object detector for detection. This suggests that, whereas improved images may somewhat enhance human visual perception, they may also cause the loss of important feature information that is necessary for object-detection tasks. Additionally, we compared the processing speed and training time of each method.