## 6 Traditional and DL Approaches for Object Detection in AVs
### DL Approaches for Object Detection in AVs
#### 6.2.2 DL Approaches for Pedestrian Detection  
A useful deformation model algorithm for managing occlusions is the Deformable Part Model (DPM), which scores blocks or sections [143]. It prevents objects from obscuring pedestrians. Classification algorithms use object features to determine which objects are pedestrians and which are not. A DL- and feature-selection-driven pedestrian detection system (PDS) is presented in [144]. The way the technology works is that a camera installed on the car's dashboard records the surroundings as a video. After that, this video data are put through a number of preprocessing stages until pedestrians are finally classified and localized. One of the paper's key findings is that the composition and quality of the datasets for training and testing that are used in the detection phase have a direct impact on the system's efficacy. The research presented in [145] demonstrates a significant advancement in nighttime human detection using visible light cameras, particularly through the application of convolutional neural networks (CNNs). The method's effectiveness is evident in its superior performance across various contexts, including open and self-constructed databases such as DNHD-DB1, KAIST, and CVC. This achievement is a notable improvement over previous techniques and is crucial for the development of intelligent surveillance systems. Another study [146] addresses the challenges of pedestrian identification in adverse weather conditions by introducing a novel DL network framework. This framework's ability to adjust to different lighting conditions through multispectral fusion and illumination perception is a clear advantage. The integration of cloud computing and the Internet of Vehicles (IoV) ecosystem further expands its potential for real-time applications in autonomous vehicles, offering a versatile and adaptable solution. Furthermore, Ref. [147] introduces an image fusion module (MSRCR-IF) that significantly enhances the Mask R-CNN-based pedestrian identification in low-light conditions. The 4.66% improvement in detection accuracy, reaching 85.05% on a self-built dataset, is a substantial gain over the existing mainstream techniques. This advancement is particularly valuable for surveillance systems operating in challenging lighting conditions. Table 5 summarizes the work. Another study [148] provides valuable insights into the challenges of data collection across diverse meteorological conditions and introduces the ZUT dataset, which boasts over 122k annotations. The research emphasizes the potential benefits of integrating Automobile CAN data with ADAS systems and suggests temperature-based normalization as a strategy for improvement. The recommendation to use 16-bit images for a 10.67% increase in detection accuracy and the consideration of car speed for parameter adjustments are noteworthy. The study also acknowledges the need for onboard precipitation sensors and a more extensive dataset that spans multiple seasons to enhance pedestrian detection accuracy, as demonstrated by the comparison with the SCUT dataset. The study in [149] focuses on metropolitan areas with a speed restriction of 48-70 km/h and showcases impressive performance on both rainy and dry roads. The method utilizes the SMP technique for depth calculation and