## 6 Traditional and DL Approaches for Object Detection in AVs
### DL Approaches for Object Detection in AVs
#### 6.2.3 DL Approaches for Road Lane Detection  
A color-based road lane-detecting system was used in research [154; 155; 156; 157]. In favorable weather, Refs. [154; 155] achieved above 90% accuracy. However, in adverse weather conditions, the accuracy considerably decreased. Some researchers used LiDAR technology for lane detection in order to reduce the impact of ambient influences on the detection algorithm. With the use of both images and LiDAR data, [PERSON] et al. [158] developed a lane detection algorithm that combines camera and LiDAR information, improving the capacity to detect lanes in difficult environmental situations. By combining data from cameras, GPS, and LiDAR, the authors in [159] were able to develop a precise distance estimation between the vehicle and lateral lane that was accurate to the centimeter. However, the high cost of LiDAR and its limited sensitivity to unfavorable weather conditions make it difficult to apply to large-scale lane detection systems. A different method [160] uses the HSV color space to achieve an accuracy of 86.21% regarding the rate of identification. This method works best in daylight or with white light because it takes advantage of the near-color correspondence between white and yellow lane markers. Consequently, global thresholding is necessary for precisely dividing color planes and obtaining lane markers in both the YCbCr and HSV color models. In contrast, in tunnel environments with distinct color illumination, the lane markers diverge from their real hues, making reliable detection difficult. [PERSON] et al. [161] developed a different technique called \"SafeDrive,\" which identifies the lane marking in areas with poor lighting. They located other distinct images of highways at the same place using the vehicle's location information, and they used those images to identify the lanes. Other researchers [162] also carried out an additional study that, because of the solid and easily visible lane markings, was able to identify lanes in several types of weather conditions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Year** & **Technique** & **Weather Classes** & **Accuracy** \\ \hline
[141] & 2023 & YOLOv3 & several weathers & 74.38\% (AP) \\ \hline
[145] & 2017 & CNN & night time & 98.3\% \\ \hline
[147] & 2021 & Mask R-CNN & lowlight & 85.05\% \\ \hline
[152] & 2022 & YOLOv5 & Day, Night & 96.6\% \\ \hline
[153] & 2023 & YOLOv7, CNN, Faster R-CNN, SSD, HOG & Rain, Fog, Snow, Sand & 0.73, 0.883, 0.71, 0.657, 0.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Pedestrian detection previous work with DL. Using already installed roadside webcams, real-time road weather and road state detection algorithms were developed in [163]. Transfer learning was utilized to train detection models utilizing three previously learned CNN architectures. When it came to accuracy, ResNet18 outperformed the other models, scoring about 97% for weather and 99% for surface conditions. These models can be useful in improving road safety since they can automatically recognize and relate real-time circumstances to road networks without the need for human intervention. They can be incorporated into advanced traveler information systems (ATIS) for drivers and utilized to optimize winter maintenance routes. Additionally, the models could replace manual reporting in snowplows, increasing driver safety and accuracy. Research [164] presents a novel approach that combines computer vision and DL to extract meteorological data from street-level photos without any image restrictions. With recognition accuracy ranging from 91% to 95.6% across several categories, the study presents four deep CNN models designed to identify a variety of visibility circumstances, including dawn and dusk, daytime, evening, glare, and weather elements like rain and snow. A study [165] presents a DL-based method for lane marker detection in order to address the difficulties associated with classifying road markers during rainy weather.