## 2 Literature Review
One approach to overcome this issue was used in the system proposed in [2] for visually impaired customers that we mentioned earlier, including samples in the training set that are only taken in perfect lighting and ideal conditions. At the same time, all tests are conducted on data gathered from videos taken of items in the stores. Thus, they are subjected to different conditions of lighting and potential distortions and occlusions. Following this training approach would usually make it hard to optimize the model and result in decreased accuracy to enhance the generalization of the detector. Powerful techniques can reduce this gap between both training and testing sets, including pre-processing and post-processing methods that benefit from the color, position, and texture information to distinguish between the targeted items, as shown in the approach proposed in [11]. The design of the architecture of the feature extraction network in the YOLOv2 algorithm should vary depending on the application and highly correlate with the model performance. Applications with a limited number of output classes in which a detector targets a certain pattern or shape can be implemented with small network architecture. For instance, the architectures of two models developed in [12], used to detect defects in \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Approach**}} & \multicolumn{1}{c}{\multirow{2}{*}{\begin{tabular}{c} **Item** \\ **Accessibility** \\ \end{tabular} }} & \multirow{2}{*}{**Self-Checkout**} & \multirow{2}{*}{**Anti-Theft**} & \multicolumn{1}{c}{\begin{tabular}{c} **Store** \\ **Infrastructure** \\ \end{tabular} } \\ \hline [PERSON] et al. [4] & - & \begin{tabular}{c} CNN (Item \\ detection) \\ \end{tabular} & Weight & High \\ \hline [PERSON] et al. [7] & - & RFID & - & High \\ \hline [PERSON] et al. [5] & Autonomous & - & - & Low \\ \hline [PERSON] et al. [6] & Autonomous & RFID & - & High \\ \hline [PERSON] et al. [9] & - & \begin{tabular}{c} YOLO (Shopping \\ behavior) \\ \end{tabular} & - & Low \\ \hline [PERSON] [8] & - & RFID & Weight & High \\ \hline Ours & \begin{tabular}{c} Semi- \\ Autonomous \\ \end{tabular} & \
\begin{tabular}{c} YOLO (Barcode \\ detection) \\ \end{tabular} &
\begin{tabular}{c} Weight, \\ SURF, \\ \& Color \\ \end{tabular} & Low \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Feature table. yarn-dyed fabric, were made of 24 layers for the first model and only 14 layers for the second. The used architectures were YOLO9000 and Tiny-YOLO, respectively, and they were compared to a third 30-layer one, called YOLO-VOC. The results showed superior performance for the YOLO-VOC, compared to the first models. However, by performing an iterative optimization of the training options, including the number of iterations, the activation function, and the learning rate, the average precision, recall, and IoU results exceeded the YOLO-VOC results, reaching 92.62%, 90.56%, and 66.36%, respectively, for the Tiny-YOLO architecture. The results were slightly better for YOLO9000. For more complicated applications, such as for the detector developed in [13], large architectures might be required when the features of the targeted objects are more complex and variable. The authors in [14] used a 30-block architecture with 22 convolutional layers to detect and distinguish between healthy and infected tomatoes. The authors increased the network's performance to achieve a mAP score of 0.91 by adjusting the learning rate during the training process as a function of the average loss. Gaussian mixture modeling (GMM) was implemented in [15] to detect moving objects. The authors applied a new approach in which image blocking was used to construct the GMM model of the background instead of using individual pixels. Although the accuracy would be reduced, such a method with optimal parameters could decrease the processing time required for constructing the background model.