## 3 Physical and Mixed Safety
### Pedestrian Safety
The model introduces multiple built-in subnetworks, which detect pedestrians with scales from disjoint ranges. Outputs from all of the subnetworks are then adaptively combined to generate the final detection results, which are robust to the large variance in instance scales via a gate function defined over the sizes of object proposals. The latest improvement to the R-CNN and Fast R-CNN algorithms eliminates the selective search algorithm for region proposals. [PERSON] et al. developed an object-detection algorithm called Faster R-CNN [69], allowing the network to learn region proposals. The Faster R-CNN algorithm takes the entire image as the input to the convolutional network, exactly like the Fast R-CNN. Still, a separate network was implemented to predict region proposals instead of a selective algorithm. An analysis of the Faster R-CNN algorithm for pedestrian detection was performed in the article [70]. The study showed the validity of using the Region Proposal Network. Another proposal to improve [71] Faster R-CNN was about improving the quality of the network and using K-means cluster analysis. The faster R-CNN method can detect people with a drone [72]. Based on this algorithm, a new FCF R-CNN model was developed [73]. It is based on feature fusion and context analysis. The proposed method has better results for pedestrians that are small in size and obscured, and it is also robust to difficult scenes. In 2015, [PERSON] et al. proposed the first single-stage detector YOLO [74]. The first version had reduced detection accuracy relative to the two-stage detector, but significantly increased the detection speed. High speed and precision are crucial for pedestrians and unusual situations on the road. Subsequent versions of the YOLO network were prepared to improve detection quality. Variations containing specific improvements were also in troduced for newer versions. The first of these concerns the YOLOv2 network [75,76]. The improvement of this network began with a modification of the DataNet53 model, in which feature creation was strengthened. In addition, three inception depth convolution modules were added and integrated at different levels. All these changes lead to a more-comprehensive characterization of the object in the image. The next version of the YOLOv3 network [77,78] uses the HOG method, which was implemented as preprocessing. This method makes it possible to highlight pedestrian contour features, especially small pedestrian target features, and reduce the interference caused by background information on the detection results. Another attempt to improve the YOLOv4 [79,80] network was based on implementing a modified detection model. The proposed model combines a new type of Spatial Pyramid Pooling (SPP) network and K-means clustering algorithm with the YOLOv4 model for easier feature extraction. In addition, the Mish activation function is applied to the neck of the detection model, replacing the Leaky ReLU activation function to improve detection performance. Another improved pedestrian detection performance solution is an updated version of MobileNet [81] combined with the Single-Shot Detector (SSD) [82,83]. This method's four components are important for pedestrian detection, feature extraction, deformation, occlusion handling, and classification. The solution proposed in the paper allows the coordination of the components to increase their strength with a reduced number of parameters. The model achieved better results when tested on low-end edge devices than different versions of the YOLO network.