# Lightweight Deep Learning for Road Environment Recognition
## 4 Experimental Results
### Experimental Result and Analysis  
Figure 14 shows the loss value plot during the training of the model and the validation loss function plot obtained by using the validation dataset to validate the model during the training process. The pre-trained MobileNet model is used to initialize the weight parameterization of the underlying shared convolutional layer. For the training of the object detection and lane detection models, the loss functions use YOLO loss and Focal loss, respectively and the training batch size was set to 8 and 4, respectively, with initial learning rates of 0.001 and 0.0001, respectively, and a total training iteration count of 100. The loss function completed convergence at 57 and 74 epochs, respectively. Firstly, to verify the optimization ability of the attention mechanism LRCA-Net proposed in this paper, two sets of control experiments were conducted with the network without adding the attention mechanism and the network with CBAM as the attention mechanism for object detection and lane detection, respectively, as shown in Figure 15. The results of the two quantitative experiments for object detection and lane detection are given in Tables 4 and 5. Taking the backbone network Mobilenetv1 as an example, mAP and mPA are 90% and 90.3%, respectively, when no attention mechanism is added. Additionally, the proposed method in this paper mAP, mPA can reach 93.2% and 93.1%, which also improves 1.8% and 1.7%, respectively compared to CBAM, while the number of parameters gets reduced to some extent. It can be no matter for object detection or lane detection, the attention mechanism method proposed in this paper can effectively improve the performance of the algorithm, and certain optimization improvements compared to CBAM. Note that our LRCA-Net obtains higher accuracy while having less model complexity. Table 6 gives a comparison of the quantitative experimental results using the same dataset and model training methods. Here, the proposed object detection algorithm proposed in this paper is compared with SSD, Faster RCNN, YOLOv4, YOLOvX series, EfficientDet series [38]. Our proposed method can achieve 93.2%, 92%, 91.8% mAP depending on the backbone network, where the model with the backbone network of MobileNetv1 is the optimal model, which is an improvement compared with other detection methods. As shown in Figure 16, the mAP is improved by 5.5% and 2.3%, respectively, compared with YOLOX-S and EfficientDet-D3, which have a similar number of parameters, for the optimal model. The volume reduction was 77%, 87.4%, and 64.8% compared to YOLOX-L, YOLOX-X, and EfficientDet-D5 with mAP at the same level. For the time factor, compared to YOLOX-L, YOLOX-X, and EfficientDet-D5, which achieve the same level of mAP but  
Figure 14: Model loss function graph. To verify the actual effectiveness of our model, the actual detection results of the proposed model in this paper are given in Figure 17. We acquired real-time road pictures of the road environment in Daegu, South Korea, through an in-vehicle camera, and most people, cars, motorcycles, and bicycles are detected with sufficient accuracy and few missed detections. Only one detection frame is output for each target, and the detection capability is good for both near and far objects, which further validates the effectiveness of our algorithm. Tables 7 and 8 give the comparison of quantitative experimental results using the same lane dataset and model training method. Here, the lane detection algorithm proposed in this paper is compared with SegNet [39], PSPNet [40], DeepLabv3+ [41], and U-Net. Our method is shown to achieve 96.4%, 94.8%, and 96.1% mPA depending on the backbone network. as shown in Figure 18, and compared to other models, our method achieves the highest accuracy rate with a much-reduced model size.