# Sustainable Parking Space Management Using Machine Learning and Swarm Theory--The SPARK System
## 3 Methodology Concept
### Advanced Computer Vision and Real-Time Parking Space Detection  
In real-time parking space detection, sensor-based approaches and computer vision-based methodologies are commonly employed [40]. For instance, [PERSON], et al. [41] developed a distributed architecture integrating magnetic sensors and smart cameras for real-time parking management in Intelligent Transportation Systems (ITS). [PERSON] and [PERSON] [42] introduced a Wireless Sensor Network (WSN)-based system using ultrasonic sensors and a tree topology to guide drivers to available parking spaces. [PERSON], et al. [43] designed a low-power wireless sensor node combining optical and magnetic sensors for vehicle detection, suitable for parking management applications. [PERSON], et al. [44] proposed an anisotropic magneto-resistive (AMR) sensor-based algorithm to detect parking occupancy. Although sensor-based approaches such as these have been widely utilized, computer vision approaches have become increasingly significant due to cost and maintenance considerations [45]. Advances in deep learning have significantly enhanced computer vision's ability to detect or segment occupied parking spaces by identifying vehicles. [PERSON], et al. [46] employed a Deep Convolutional Neural Network (DCNN) model to detect parking slot corners and classify occupancy. [PERSON], et al. [47] applied a Faster Region-based Convolutional Neural Network (Faster R-CNN) to identify parking slots in surround-view images. [PERSON], et al. [48] utilized a Residual Network (ResNet50) and a Visual Geometry Group Network (VGG16) for occupancy detection. These studies exemplify the potential of deep learning methods in parking detection, although challenges such as real-time applicability remain. Other approaches, such as Mask Region-based Convolutional Neural Networks (Mask R-CNNs) [19], have been utilized to classify parking spaces into occupied, empty, and partially occupied categories. Semantic segmentation combined with vertical grid approaches [49] and evidence filters for enhancing Around View Monitoring (AVM)-based systems [50] also demonstrate the potential for parking space detection innovation. Among the deep learning methods, Convolutional Neural Network (CNN)-based techniques are particularly popular, with single-stage models such as YOLO, Single Shot MultiBox Detector (SSD), Faster R-CNN, and RetinaNet being especially notable for their speed and efficiency in real-time applications [51]. The YOLO algorithm is widely recognized for its superior performance in real-time image-based tasks. Numerous studies have consistently demonstrated its advantages over other methods, particularly in terms of speed and real-time efficacy. For example, [PERSON], et al. [52] and [PERSON], et al. [53] compared YOLOv3 with Faster R-CNN for vehicle detection in Unmanned Aerial Vehicle (UAV) imagery, concluding that YOLOv3 offers superior processing speeds while maintaining competitive accuracy. [PERSON], et al. [54] highlighted YOLOv5's efficiency in overlapping object detection, outperforming Faster R-CNNs in both speed and precision. [PERSON], et al. [55] comprehensively analyzed YOLO versions (v5 through v8), identifying YOLOv8 as the optimal balance of speed and accuracy for video surveillance. Numerous studies have specifically employed YOLO for parking space detection, achieving outstanding results. [PERSON], et al. [56] utilized YOLOv5 for real-time parking management. [PERSON], et al. [57] applied YOLOv5 for UAV-based vehicle detection, illustrating its scalability for large-scale applications. [PERSON], et al. [58] leveraged YOLO to detect street parking from dashboard camera videos, showcasing its adaptability across varied data sources. [PERSON], et al. [59] demonstrated YOLOv7's efficiency in UAV orthomosaics. [PERSON], et al. [60] combined a Faster R-CNN with YOLOv3 to enhance detection precision and speed in IoT-enabled systems. [PERSON], et al.