## 2 Related Work  
This literature review focuses on critical academic studies on enhancing object detection, classification, and customer experience in retail settings using advanced machine learning and deep learning techniques. In [16], the authors propose improving clothing image classification for e-commerce applications, such as online shopping and recommendation systems. They use deep learning models, including LeNet and ResNet, and integrate ResNet into the backbone of the YOLOv5 model, achieving over 98% accuracy on the FashionMnist dataset. While this study improves classification accuracy, it also highlights the limitations of prior neural network models before the YOLOv5 upgrade. Similarly, Ref. [17] focuses on improving object detection in unmanned retail environments using the YOLOv5 model. The authors propose a three-stage approach to reduce digital processing load: frame detection to identify frames in video sequences, low-quality image filtering, and YOLOv5 for real-time object detection. This approach, tested on the RPC dataset, shows high accuracy and speed, enhancing operational efficiency in retail settings. In [18], the authors present an affordable security solution for small retailers using a single-board computer and the YOLO algorithm. The system, featuring a camera and Raspberry Pi, differentiates between regular visitors and potential intruders, offering an economical alternative to high-cost security systems. This technology not only enhances security but also improves customer interaction. The study in [19] explores consumer buying behavior using machine learning and RFID technology. The research demonstrates how real-time RFID signal strength data can be linked to customer behavior, such as browsing and product interaction. Machine learning algorithms are shown to accurately distinguish various purchasing behaviors, providing retailers with actionable insights for improving customer satisfaction and inventory management. Finally, Ref. [20] introduces a template-free method for product detection in retail environments, addressing the challenges of traditional template-based systems. By using zero-shot learning and partitioning shelf images, this method surpasses traditional approaches regarding recall and performance, offering a more efficient and adaptable solution for product detection. In [21], the authors examine a real-time vehicle tracking technique using the YOLOv5 and DeepSORT algorithms, which is focused on the challenge of tracking vehicles in complex and dynamic traffic environments. The authors address precision problems in identifying vehicles, including fast environmental modification and vehicle similarities. To enhance vehicle tracking performance, they introduce YOLOv5_DSC, which is a simplified variant of YOLOv5s that lowers computing demands without appreciably sacrificing accuracy. The authors state that in terms of criteria like mean average precision (mAP0.5), precision, and recall, YOLOv5_DSC performs almost as well as YOLOv5s. In another study [22], the authors present a new vehicle detection and tracking approach by merging YOLOv5 with DeepSORT. This method's capacity to attain high accuracy and quick processing rates is one of its main benefits, which makes it ideal for real-time traffic control applications. While DeepSORT improves tracking efficiency and enables efficient surveillance in complex situations with many moving objects, YOLOv5 is skilled at rapidly spotting vehicles. This integration can significantly enhance traffic flow and provide more environmentally friendly urban transportation options. The accuracy of tracking and detection might be jeopardized if the training data are inadequate or not indicative of real-world conditions. Furthermore, it might be challenging to meet the computational needs of running two algorithms simultaneously, particularly on devices with limited computing power.