## 3 The Proposed Model
### Object Recognition Module  
At the initial stage, the objects that exist in the frame are recognized by the multi-scale Faster RCNN model. Actually, the noticed object is lower in resolution and lesser in size. The present method (e.g., Faster-RCNN) has an optimum detection accuracy for huge objects and could not efficiently detect small objects from the images [19]. An essential purpose is that the individual methods dependent upon DNN create the image computedwith convolutional and down-sampled methods for achieving further abstract and higher-level features. All of the down-sampling causes the images to decrease by half. When the object is the same as the size of objects from the PASCAL VOC, the detail of an object's feature is attained with this convolutional and down-sampling method. However, when the detection of the object is on a very low scale, the last feature is only left with 1-2 pixels after several down-samplings. Thus, some features do not completely describe the features of objects, and the current detection techniques are not efficiently detecting the lesser target objects. The deeper convolutional function further abstracts the object feature that signifies the higher-level features of objects. The shallow convolutional layer is only extracting the low-level feature of objects. For obtaining higher-level and abstract object features and ensuring that there are sufficient pixels for describing small objects, it integrates the feature of distinct scales to ensure the local details of the objects. This technique has further robust features. The multi-scale Faster RCNN technique was separated into four parts: a primary part is a feature extracting layer that has five convolutional layers (red part), five ReLU layers (yellow parts), two pooling layers (green parts), and three RoI pooling layers (purple part). It can normalize the results of the 3 rd, 4 th, and 5 th convolutional, correspondingly. Figure 1 illustrates the structure of the RPN. Afterward, the normalizing outcome is sent to the RPN layer and feature-combining layer to the generation of the proposal region (PR) and extracts the multi-scale feature correspondingly. The second part was the feature-combination layer, which joins the distinct scales features of the third, fourth, and fifth layer, as in the 1D feature vector with connection function. The tertiary part was the RPN layer that mostly understood the generating of PRs. The final layer was utilized for realizing the classifier and bounding box regression of the object, which is in PR, and was collected from softmax as well as the bounding box. Obtaining the combinatorial feature vector requires normalizing the feature vector of the distinct scales. Generally, the deeper the convolutional layer output the lesser the scale feature. Conversely, the lesser the convolutional layer outcomes the better the scale features. The feature scale of distinct layers is widely distinct. The weight of large-scale features is significantly greater than that of smaller-scale features under the network weight that is tuned when the feature of these various scales was integrated, causing the minimum detection accuracy. To prevent large-scale features from covering small-scale features, the feature tensor, which is the output in the distinct RoI pooling, can be normalized before  
Figure 1: RPN Architecture. an individual's tensor is concatenated. It can be utilized when L2 is normalized. The normalized function that is utilized for processing all of the pooled feature vectors was placed afterwards RoI pooling. After normalization, the scale of feature vectors for the third, fourth, and fifth layers are normalized as a unified scale. \[\hat{X}=\frac{X}{\|X\|_{2}}, \tag{1}\]  
\[\|X\|_{2}=(\sum_{i=1}^{d}|x_{i}|)^{1/2}, \tag{2}\]  
where \(X\) implies the novel vector from third, fourth, and fifth layer, \(\hat{X}\) has normalizing feature vectors, and \(D\) signifies the channel number of all RoI pooling. \[Y=\gamma\hat{X}, \tag{3}\]  
where \(Y=[y_{1},\;y_{2},\;\ldots,y_{d}]^{T}\).