## 4 Experiments  
This section describes the dataset used to perform the proposed work and compare the same with the existing work. The PASCAL VOC 2007 dataset was considered for the experiments [7]. This dataset consists of 20 classes (as mentioned below) for object recognition; it provides some sets of standardized image datasets. A common set of tools is provided to access the datasets as well as the annotations. This dataset was available from the VOC challenge links. There are mainly two competitions in it, namely, classification and detection. The detection seeks to forecast the bounding box and the class label from the target classes in the test image, while the classification aims to predict the absence/presence of the class example in the test image [7]. The Table 2 below compares the various object detection approaches implemented in the PASCAL VOC 2007 dataset and compares speed. It was observed that the mAP of the proposed model was far better as compared to the  
Figure 10: Flowchart of the training and detection phase of the proposed model. Figure 9: Efficient multi-scale anchor box (Example 2). other object detection methods. The proposed model achieves a speed of 11 FPS over the baseline models on the Pascal VOC dataset. In Table 3, the existing model is compared with the proposed model in terms of various parameters such as the grid size, several convolution layers, and the number of bounding boxes drawn per grid cell over the same baseline dataset, i.e., the Pascal VOC dataset. The existing YOLO model uses the \(7\times 7\) grid to divide the entire image, and the architecture of the YOLO model is such that it uses 24 convolutional layers. In YOLO, there was no concept of an anchor box used; however, in YOLO v2, the concept of an anchor box was introduced for object detection. Two bounding boxes per grid cell were used in YOLO, and the entire result was compared using the Pascal VOC dataset. The proposed model used the grid of 19 \(\times\) 19 over the entire image. The number of convolutional layers was reduced from 24 to 22, and a multi-scale anchor box was introduced in our proposed model. Instead of using two bounding boxes per grid cell, the proposed model used nine bounding boxes per grid cell, with each bounding box having nine multi-scale anchors. The importance of using the multi-scale anchor box was to precisely detect the small objects and to detect and recognise the large-sized objects. Table 4 shows that when the grid size was 19 \(\times\) 19, the mAP obtained was 84.49, which is reasonably better than the other mAP of varying grid sizes. Table 5 considers the class-wise performance of the proposed model with the existing algorithms over the baseline Pascal VOC dataset. In the above-mentioned table, the average precision of every \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sr. No.** & **Model** & **Year** & **mAP (\%)** & **Speed (FPS \({}^{*}\))** \\ \hline
1 & R-CNN & 2014 & 66.0 & 0.05 FPS \\ \hline
2 & Fast R-CNN & 2015 & 70.0 & 0.5 FPS \\ \hline
3 & Faster R-CNN & 2015 & 73.2 & 7 FPS \\ \hline
4 & Mask R-CNN & 2017 & 78.2 & 0.1 FPS \\ \hline
5 & YOLO v1 & 2016 & 63.4 & 14 FPS \\ \hline
6 & YOLO v2 & 2017 & 76.01 & 12 FPS \\ \hline
7 & YOLO v3 & 2018 & 81.7 & 9 FPS \\ \hline
8 & YOLO v4 & 2020 & 83.5 & 10 FPS \\ \hline
9 & YOLO v5 & 2021 & 84.12 & 6 FPS \\ \hline
10 & RefineDet512 & 2018 & 81.8 & 6 FPS \\ \hline
11 & CenterNet & 2019 & 78.7 & 0.3 FPS \\ \hline
12 & CenterNet2 & 2021 & 81.69 & 2 FPS \\ \hline
13 & Proposed Model & 2021 & **84.49** & 11 FPS \\ \hline \hline \end{tabular} \({}^{*}\) indicates that the result of FPS is calculated by running the algorithms on 4 GB GPU, NVIDIA GTX 1050 Ti.  
\end{table} 
Table 2: Comparison of existing models with a proposed model in terms of mAP and speed.