## 2 Literature Review
To address these issues, researchers have introduced several improvements, such as multi-scale and attention mechanisms, to enhance small object detection capabilities and more efficient feature fusion techniques to improve overall performance [12]. Despite some challenges, SSD remains a powerful tool for fast and accurate object detection, making it suitable for applications where speed is essential. For instance, [PERSON] et al. [13] utilized the SSD model to improve gesture recognition systems, particularly in medical and educational settings where gestures facilitate communication for diagnosis and treatment. Similarly, [PERSON] et al. [14] explored the SSD model's application in medical image analysis, focusing on the automatic detection of polyps, which demonstrates the SSD model's potential in medical diagnostics. The MobileNet model has garnered significant attention due to its efficient visual recognition performance, particularly on mobile and embedded devices. These models utilize depthwise separable convolutions to construct lightweight deep neural networks, effectively balancing latency and accuracy, making them ideal for real-time processing applications. Regarding MobileNet's performance, it excels in object detection and classification, especially when handling complex images and video data [15]. For instance, MobileNetV2 introduced linear bottlenecks and shortcut connections, which contributed to improved training speed and accuracy. These models maintain accuracy similar to larger models while keeping computational and parameter requirements low. For example, [PERSON] et al. [16] applied deep learning techniques, particularly the MobileNet architecture, to detect bean leaf diseases. The model's effectiveness was evaluated by testing it on three different bean leaf image datasets, achieving a high accuracy of over 92% on all three datasets. [PERSON] et al. [17] proposed a novel lightweight model called MobileNet-SA for sketch classification tasks. This model combines the self-attention mechanism with the lightweight Convolutional Neural Network architecture of MobileNet to enhance sketch classification performance in resource-constrained environments. In another study, transfer learning techniques were employed to predict the severity of traffic accidents [18]. Shapley values were used to analyze the impact of different features on model predictions, aiming to provide more effective traffic safety policy recommendations. The experiments utilized various deep learning models (Multilayer Perceptron, MLP; Convolutional Neural Network, CNN; Long Short-Term Memory, LSTM) and transfer learning models (ResNet, EfficientNetB4, InceptionV3, Xception, MobileNet), with MobileNet achieving the best performance, with a prediction accuracy of 98.17%. The advantages of MobileNet in visual recognition include high efficiency, low latency, and relatively high accuracy, though it may perform less effectively in identifying small objects in certain situations. These models are particularly well suited for mobile and edge computing devices, capable of handling a wide range of complex visual recognition tasks [19; 20]. YOLO (You Only Look Once) is a widely used deep learning algorithm known for its speed and accuracy in real-time object detection. Below are some of the latest research and applications of YOLO in various fields. YOLO has been applied in intelligent surveillance systems, particularly in real-time object detection, demonstrating its advantages in speed and precision, making it an ideal choice for smart cities and security systems. Studies [21; 22] have explored the application of YOLO in intelligent surveillance systems, showcasing its efficiency and accuracy in real-time object detection. In the field of traffic sign detection and recognition, YOLO has proven crucial for enhancing vehicle safety and the reliability of autonomous driving technologies. These systems can instantly identify and classify traffic signs, assisting drivers or autonomous systems in making better driving decisions.