## 3 Methods
### Proposed Architecture  
In our proposed architecture, there are 22 convolution layers and 5 max-pooling layers. The first 16 convolution layers are used for extracting the features from the input image. The remaining 6 convolution layers are used for object detection which is depicted in Figure 7. It trains on a 608 \(\times\) 608 image size. Mostly 3 \(\times\) 3 and 1 \(\times\) 1 filters are used, and they are double the number of filters after every pooling step. For end-to-end detection methods, the quality of the anchor box plays a vital role. The anchor boxes can be generated \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Highlights** & **Limitations** \\ \hline \multirow{5}{*}{**R-CNN**} & Introduced for obtaining better accuracy as compared to HOG. Uses & \multirow{5}{*}{Training is expensive in time and space. Testing is slow.} \\  & selective search because CNN cannot run on too many patches & \\  & created by the sliding window detector. The first model for & \\  & integrating the RP methods with the CNN; improvement in the & \\  & performance observed with respect to the previous & \\  & state-of-the-art methods. & \\ \hline \multirow{5}{*}{**Fast R-CNN**} & end-to-end detector training; design of a layer of RoI pooling; the & \multirow{5}{*}{Takes more execution time for real-time applications.} \\  & multi-task objective of having both classification head and bounding box regression head reduces the overall training time and increases & \\  & the accuracy. & \\ \hline \multirow{5}{*}{**Faster R-CNN**} & Instead of a selective search approach, an RPN is proposed for the & \multirow{5}{*}{Complex training and falls short of real-time.} \\  & generation of high-quality region proposals. Introduces invariant & \\  & translation and multi-scale anchor boxes as references in RPN. & \\  & Comparatively faster in magnitude than Fast R-CNN without the loss & \\ \hline \multirow{3}{*}{**Mask R-CNN**} & Extends Faster R-CNN by adding a branch to predict the object mask & \multirow{3}{*}{Falls short of real-time application.} \\  & along with the existing branch for bounding box prediction; & \\  & outstanding performance. & \\ \hline \multirow{3}{*}{**YOLO (You** **Only Look** **Once**)**} & The classification score for each box is predicted for every class in training. Image is divided into grids having the coordinates—SXS, & \multirow{3}{*}{Struggles for a small object.} \\  & and hence total boxes predicted are SXSXN. It sees the complete & \\  & image at once. & \\ \hline \multirow{3}{*}{**YOLO v1**} & A first efficient unified detector, significantly faster than other & \multirow{3}{*}{A decrease in accuracy as compared to the state-of-the-art detectors and struggles for small object detection.} \\  & previous detectors. & \\ \hline \multirow{3}{*}{**YOLO v2**} & Uses the number of existing strategies to improve the accuracy & \multirow{3}{*}{Not good in detecting small objects.} \\  & and speed. & \\ \hline \multirow{3}{*}{**YOLO v3**} & At three different scales, detection is carried out by applying the 1 \(\times\) 1 detection kernel to feature maps of three different sizes at three & \multirow{3}{*}{An increase in the grid size results in an increased number of anchor boxes which leads to an increase in the number of times for detection.} \\  & separate locations in the network. & \\ \hline \multirow{2}{*}{**RefineDet 512**} & Refines the sizes and the location of the anchor box with the help of & \multirow{2}{*}{As it is a two-step cascaded process, real-time detection is slow.} \\  & the anchor refinement module and object detection module. & \\ \hline \multirow{3}{*}{**CenterNet**} & Detects each of the objects as triplets, which improves both the recall & \multirow{3}{*}{Added an extra branch to identify the centre key point, which potentially} \\  & and the precision. It explores the central part of the proposal, which & \\ \cline{1-1}  & is the region close to the geometric centres & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of methods of object detection.