## 5 Performance Analysis of ReSTiNet
As a result, the net depth attains a value of 29, which is exactly twelve layers deeper in physical depth compared to Tiny-YOLO thereby raising the network's accuracy. All max-poolings are set to \(3\times 3\) in size followed by the down-sampling technique later within the architecture. This in turn yields several layers with large activation maps [61]. Such layers provide activation maps with a minimum of \(1\times 1\) spatial resolution and typically in higher orders at other times. Activation maps' width and height can be determined using a set of variables, namely the input data size and various choices of layers in which down-sampling more likely tends to occur. The down-sampling strategy has been accomplished in studies [62; 63; 64] using a stride that is larger than one during a choice of convolutional or pooling layers. It was concluded that a large number of layers contain smaller activation maps when the initial layers are set to larger stride parameters. The authors in [65] detected improved classification accuracies after implementing down-sampling strategies into four distinct CNN networks [53]. Then, residual connections are integrated to examine whether it can increase the efficacy of the Tiny-YOLO network while making the model quicker and smaller at the same time. The concept of the fire module [23] is modified by adding residual connections at strategic locations across the network. The model does not experience an increase in complexity apart from a bit of computation associated with the collection operation as the residual connections do not have any parameters. This model employs the dropout layer to handle the over-fitting issue and speed up data processing. The dropout method disregards the randomly chosen neurons during the training period. Figures 6 and 7 show the detection results for both proposed ReSTiNet and Tiny-YOLO models. From all figures, it can be seen that the proposed model detects human objects with a higher accuracy, while Tiny-YOLO can sometimes miss objects and recognize non-human objects as human. These scenarios are shown in Figures 6c and 7b. The proposed ReSTiNet sometimes misses people in a dense scenario shown in Figure 7a. In this scenario, there are five people on the wall. Of these five people, ReSTiNet detected only four. However, the detection rate is still better than that of Tiny-YOLO, which detected only two out of the five people, but the proposed method can still be improved. The images showing the results of the detection are available in full size at the following URLs: Figure 6: [[https://i.ibb.co/6](https://i.ibb.co/6) FhDYf5/P1-comp.png]([https://i.ibb.co/6](https://i.ibb.co/6) FhDYf5/P1-comp.png) (accessed on 4 September 2022); Figure 7: [[https://i.ibb.co/tDs6](https://i.ibb.co/tDs6) xPB/P2-comp.png]([https://i.ibb.co/tDs6](https://i.ibb.co/tDs6) xPB/P2-comp.png) (accessed on 4 September 2022). The ReSTiNet architecture that has been suggested has a significantly reduced number of parameters while also preserving a greater amount of information flow throughout the model. It detects humans more quickly than other lightweight models, and its performance in terms of detection time and mAP score is superior to that of those models. This is despite the fact that the model itself is quite compact.