# Sustainable Parking Space Management Using Machine Learning and Swarm Theory--The SPARK System
## 5 Discussion and Conclusions  
The results indicate a high level of performance from both the vehicle detection and parking detection subsystems. However, while vehicle detection achieved consistent performance across metrics, parking space detection exhibited more variability. This variability is likely due to the inherent complexity of parking spaces, which often require more nuanced interpretation compared to the clearer and more distinct vehicle outlines. A significant challenge encountered in parking detection involves fitting predefined polygons to match real-world parking spaces using the ABC algorithm. This task becomes more complicated when parking spaces are irregularly shaped or constrained, leading to errors in correctly identifying available spaces. Additionally, relying on fixed camera positions, currently lacking verification of the invariability of this parameter, and predefined parking polygons may fail to account for dynamic changes in parking layouts or unexpected obstructions, limiting the scalability of the system. Given the growing demand for efficient parking management in urban areas, the technical innovations provided by the SPARK system represent a notable contribution to addressing the parking availability problem. The integration of advanced deep learning algorithms, such as the YOLOv9e Seg, along with swarm optimization techniques, allows the system to operate effectively under various conditions. In the SPARK system, the YOLOv9e Seg algorithm identifies vehicles, and the ABC algorithm uses this information to determine optimal vacant parking spaces. This dual approach, combining image recognition with optimization techniques, strengthens the system's ability to provide accurate, real-time information on parking availability. Moreover, the use of crowdsourced data can enhance the system's ability to adapt and improve over time by generating larger training datasets, leading to continuous improvements in detection and optimization. However, the system also faces challenges that need to be addressed. For instance, maintaining data quality and ensuring user privacy, especially with the use of crowdsourced data, are significant concerns. Crowdsourced data introduce variabilities in quality, requiring robust validation mechanisms and reputation-based scoring systems to ensure reliability. Furthermore, the system's performance can be affected by environmental factors, such as weather conditions, varying lighting conditions, or complex parking scenarios, which could compromise the accuracy of parking space detection. Tests conducted under different conditions--day and night--indicated that street lighting in the test area helped maintain consistent segmentation performance for vehicles, although parking space detection remained susceptible to environmental influences. One key observation from the results is the discrepancy in recall and precision between the YOLOv9e Seg model and the SPARK system. The vehicle detection subsystem displays stable performance across all metrics, whereas parking detection shows higher variability. This could be attributed to the more subtle visual indicators required for identifying available spaces. Scenarios with lower parking space density, in particular, tend to generate more inaccuracies in detection, likely due to overlapping objects or unconventional parking arrangements. Moreover, the ABC algorithm's reliance on predefined penalty parameters limits its ability to adapt to diverse parking scenarios, suggesting the need for dynamic real-time parameter tuning or the preparation of multiple models, which could be alternately activated within the system depending on specific conditions affecting the system's performance. These cases challenge the robustness of both the YOLOv9e Seg model and the ABC algorithm, indicating the need for more adaptive techniques that can account for occlusions or partial detections in complex environments.