## 2 Related Works
### Object Detection Method
The YOLOv3 model consists of 53 convolution layers and 23 residual blocks, and it uses darknet-53, FPN for the neck, and YOLO for the head as the backbone network. However, the YOLOv3 model does not improve performance more than the YOLOv2 model. YOLOv3 shows a high FPS speed at the time when using the graphics processing unit (GPU) while maintaining accuracy. Table 1 shows the performance comparison between YOLOv3 and previous algorithms. Finally, the YOLOv4 model uses CSPDarknet-53, which combines Cross Stage Partial Network (CSPNet) with Darknet-53 as its backbone network. Although similar in structure to the YOLOv3 model, it combines techniques such as Bag of Freebies (BOF) and Bag of Specials (BOS) to improve the speed and accuracy. Spatial pyramid pooling (SPP) was used to compress the features of the image without distortion to preserve information, and PAN was used instead of a feature pyramid network (FPN) to reduce the flow of information and preserve low-level information. The result was robust real-time performance and improved performance for small objects. Compared with YOLOv3, YOLOv4 increased the AP by 10% and FPS by 12%. Also, the YOLO model has been used for objection detection in a range of applications. Ref. [21] presented a system for monitoring litter in rivers using object recognition technology and unmanned aerial vehicles (UAVs). This system has several advantages, namely preventing the pollution of riverbanks with litter and enabling the management of hard-to-reach areas. The YOLOv5 algorithm and scale-invariant feature transform (SIFT) were used for the litter detection model. Ref. [22] developed a framework based on visuals to prevent heatstroke fatalities in confined vehicles. The mechanism identifies individuals or animals present inside a vehicle and communicates with the caregiver via notifications in case of their absence from the car. For interior detection, an artificial intelligence model was \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Detection Algorithm** & **mAP** & **mAP-50** & **Inference Time** \\ \hline SSD321 & 28.0 & 45.4 & 61 \\ DSSD321 & 28.0 & 46.1 & 85 \\ R-FCN & 29.9 & 51.9 & 85 \\ SSD513 & 31.2 & 50.4 & 125 \\ DSSD513 & 33.2 & 53.1 & 156 \\ FPN FRCN & 36.2 & 59.1 & 172 \\ RetinaNet-50-500 & 32.5 & 50.9 & 73 \\ RetinaNet-101-500 & 34.4 & 53.1 & 90 \\ RetinaNet-101-800 & 37.8 & 57.5 & 198 \\ YOLOv3-320 & 28.2 & 51.5 & 22 \\ YOLOv3-416 & 31.0 & 55.3 & 29 \\ YOLOv3-608 & 33.0 & 57.9 & 51 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of YOLOv3, RetainNet, FRCN, SSD, and R-FCN [13]. created, utilizing NanoDet, YOLOv6, and YOLOv7. Ref. [23] proposed an assistive system based on the Artificial Intelligence of Things (AIoT) framework to provide convenience to blind people. The system is based on smart glasses and a lightweight network based on YOLOv5, which is capable of detecting external objects and surrounding objects such as books. Ref. [24] proposed a system for tracking and classifying objects in a warehouse by combining YOLOv5 for object detection with Deep Simple Online and Real-Time Tracking (DeepSORT) to classify people, goods, and environments. The algorithm demonstrated its robustness and efficiency in a multi-object tracking (MOT) challenge. Ref. [25] proposed a method which models the bounding box of YOLOv3 with Gaussian parameters and redesigns the loss function to lower the rate of false positives in object detection algorithms for autonomous driving. This method significantly reduces false positives (FPs) and increases true positives (TPs), resulting in an improvement in the mAP by 3.09 and 3.5 on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) and Berkeley DeepDrive (BDD) datasets, respectively. Demonstrating real-time detection performance, it achieved a high inference rate of 42 FPS. Ref. [26] introduced a new loss function for bounding box regression that provides optimization for both bounding box transformation and localization variance.