## 5 Conclusions and Future Works  
In our study, we examined the nighttime detection-related behaviors of our system. Our system can tolerate severely blurred objects in images taken under urban nighttime lighting. However, for extremely dark conditions with no vehicle headlights or taillights illuminating the scene, the system can recognize the outlines of objects only if the objects are clear. Our labeling conventions pertain to occluded objects, blurry objects, and objects occupying small numbers of pixels. The results indicate that in extremely dark conditions, our innovative approach to feature extraction outperforms traditional methods [7; 8], which rely heavily on the illumination from vehicles' headlights and taillights. We also discovered that networks with shortcuts, such as ResNet101 [2], have a suitable nighttime detection performance. ResNet101 was used in our system as a feature extractor. Systems containing networks with shortcuts as feature extractors can accurately detect partially visible objects and relatively small objects during nighttime. Nevertheless, for images with a size of \(500\times 375\) pixels, the system with ResNet101 [2] required nearly twice the processing time of the system with VGG16 [1]. For embedded systems, the image size should be reduced to achieve real-time detection with a machine having relatively limited computational ability. Our study presents labeling methods and systems optimized for occluded objects, small objects, and blurry objects, especially in extremely dark conditions. We obtained considerable improvements and optimization in terms of the performance levels. The experimental results indicate that the models trained with our nighttime data sets, which were labeled according to our conventions, could detect obscure, occluded, and small objects during nighttime or under insufficient illumination. In extremely dark conditions with nearly no illumination or extremely weak lighting, our methods provided satisfactory detection performance levels. The detection performance of the proposed methods was higher than that of the original methods. The mAP values increased from approximately 0.2 to 0.8497 with a speed of 16 FPS when processing images with a size of \(500\times 375\) pixels. This result was visually and subjectively confirmed through visual comparison of the output images. Our proposed method can effectively detect vehicles in various urban nighttime environments and under extremely dark conditions. In our future work, we plan to improve the performance of our system by using alternative normalization methods. We plan to focus on the images taken under extreme illumination conditions. We plan to develop suitable metrics to appropriately measure the model performance for localizing objects, specifically for our nighttime data. [PERSON] contributed towards the improvement of the system, the training process, and the analysis of the system, and designed the experiments. [PERSON], [PERSON], and [PERSON] performed the experiments, conducted the experiments, analyzed the experimental data, and provided the analytical results. [PERSON] conducted the experiments and validations of the experimental data. [PERSON], [PERSON], and [PERSON] collected nighttime data, set up the labeling rules, and performed data labeling. [PERSON] and [PERSON] wrote the paper. [PERSON], the supervisor of [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] proofread and revised the paper, provided guidance throughout the entire preparation of the manuscript, and gave practical advice throughout the whole research process. All authors read and approved the final manuscript. This research was funded by the Ministry of Science and Technology of Taiwan under the grant numbers MOST-107-2218-E-027-018 and MOST-108-2221-E-027 -066. The labeling of data for this study was partially supported by the members of the Video Coding and Transmission Lab at National Taipei University of Technology. Their contributions were greatly appreciated. This manuscript was edited by Wallace Academic Editing.