# Lightweight Deep Learning for Road Environment Recognition
###### Abstract  
With recent developments in the field of autonomous driving, recognition algorithms for road environments are being developed very rapidly. Currently, most of the network models have good recognition rates, but as the accuracy rate increases, the models become more complex and thus lack real-time performance. Therefore, there is an urgent need to propose a lightweight recognition system for road environments to assist autonomous driving. We propose a lightweight road environment recognition system with two different detection routes based on the same backbone network for objects and lane lines. The proposed approach uses MobileNet as the backbone network to acquire the feature layer, and our improved YOLOv4 and U-Net allows the number of parameters of the model to be greatly reduced and combined with the improved attention mechanism. The lightweight residual convolutional attention network (LRCA-Net) proposed in this work allows the network to adaptively pay attention to the feature details that need attention, which improves the detection accuracy. Finally, the object detection model and the lane line detection model of this lightweight road environment detection system evaluated on the PASCAL VOC dataset and the Highway Driving dataset show that their mAP and mIoU reach 93.2% and 93.3%, respectively, achieving excellent performance compared to other methods.  
object detection; lane detection; attention mechanism; lightweight network +
Footnote †: journal: _Sci._  
Footnote †: journal: _Sci.  
used in the feature extraction stage are Haar, histogram of oriented gradient (HOG), local binary pattern (LBP), aggregated channel features (ACF), and other operators, and the common classifiers are SVM, boosting, random forest, and so on. With the Adaboost-based face detection method [1], the object detection algorithm has experienced the traditional framework of manually designed features plus shallow classifiers and reached the pinnacle of traditional object detection. However, the traditional sliding window technique used for object detection needs to handle thousands of windows and has low performance without optimization strategies. Secondly, because of the manual design of features, it cannot express the characteristics of the object in more detail, resulting in a lower recognition rate. So, after 2013, the whole academic and industrial communities gradually started using convolutional neural network (CNN) to do object detection.  
On the other hand, classical image processing and computer vision approaches typically divide lane line recognition algorithms into four distinct steps. A considerable amount of background noise and extra pixel information may be removed by first establishing an orthogonal system concerning the body and the road surface. Then, the lane detection zone can be calculated. Image enhancement techniques such as smoothing, sharpening, and other methods are used in the second step of lane line feature extraction to improve the quality of the image. Examples include image-enhancing algorithms for the night, fog, or shadow photos [2; 3; 4]. The extraction of image features is the third step in the procedure. Image-based algorithms for lane line detection rely primarily on picture characteristics such as lane line shapes, pixel gradients, and color cues to identify lane lines [5; 6; 7]. In the end, some straight lines or curves are used to match the lane lines [8; 9; 10]. To employ these classic methods, the filtering operator must be de-tuned, and the algorithm's parameters manually adjusted based on the features of the street scene that is being targeted by the algorithm. In addition, the recognition of lane lines fails when the driving environment undergoes major changes. To meet the ever-increasing demands for lane line recognition precision, the procedure becomes more complex. Therefore, image processing and computer vision technologies will be phased out in favor of semantic segmentation methods, which have only recently begun to be researched.