## 2 Background Study
### Building Blocks of CNN
#### 2.1.5 YOLO Versions  
In YOLO (You Only Look Once) in the Figure 5, an image is fed to the single neural network and trained from scratch, thereby predicting the bounding box and class labels for each bounding box [12]. In this, the entire image is divided into grid cells, and each cell has the responsibility of predicting a bounding box only if the centre of the bounding box falls within it. The predicted bounding box involves x and y coordinates along with the width, height, and confidence. For each cell, a class prediction also plays a vital role. Figure 4: Architecture of R-CNN. For image classification, an image is fed as an input to the neural network, and a single class label along with the associated probability is obtained as the output. The class label characterizes the entire image. For object detection, either the (x, y) coordinate for each object or the list of the bounding boxes is required. Additionally, with each bounding box, the class label should also be associated. The class label and the confidence score or the probability associated with each bounding box are obtained as the output. In this, the sliding window concept is utilized wherein the slide from either left to right or from right to left is performed to localize the objects at different locations. At varying scales, an image pyramid is used to detect the objects. Classification is carried out via a pre-trained CNN. At every stop, the region of interest is calculated and is fed as an input to the CNN to obtain the output classification for the region of interest. In the case where the probability of classification of the label is high compared to the threshold, the bounding box of the ROI is labelled as L. Continuing this process, we obtain the object detectors. Finally, NMS is applied to the bounding box in order to yield the final output. In general, this method is slow, while allowing a few errors in. This method can turn any random image classification network into an object detector. Thereby, it can avoid the need to train an object detector explicitly. [PERSON] et al. introduced a hybrid approach for real-time object detection. They combine three fundamental strategies to decrease frame scanning. The Recursive Density Estimation (RDE) technique selects the scan frames. The YOLO algorithm detects and recognises the objects in the selected frame with a 97% accuracy. The SURF algorithm tracks the detected items in consecutive frames [13]. The mean average precision (mAP) is used to assess the accuracy of the deep learning object detector. It is based on the intersection of all classes in our dataset (IoU) in the Figure 6.  
[PERSON] et al. [14] introduced YOLO v2 and improvements were made in batch normalization, high resolution of the classifier, the use of anchor boxes, and the dimensionality clusters [15]. By adding the batch normalization to the architecture, the convergence of the model was increased, which led to faster training. The need for applying Dropout was eliminated. It was observed that there was an increase of 2% in the mAP from the basic YOLO. The input size in the previous YOLO was 224 \(\times\) 244 during the training time, but during the time of detection, the image could be up to-the size 448 \(\times\) 448. In YOLO v2, the fully connected layer was removed, and instead, the anchor boxes were added to predict the bounding box. With this, a decrease in the mAP was observed; however, the recall was found to increase. Figure 5: S \(\times\) S grid distribution and class distribution map of YOLO. YOLO v3 [16] utilizes the concept of multi-label classification. [PERSON] et al. utilized YOLOv3 to detect things in interior environments like offices or rooms and to generate speech messages in Arabic sound for each object observed. The presented approach can detect six objects with a 99% accuracy [17]. Binary cross-entropy is used for every class label in YOLO v3, and a reduction in the computation complexity is seen by avoiding the softmax function. The cost function is also calculated differently in YOLO v3.