## 6 Traditional and DL Approaches for Object Detection in AVs
### DL Approaches for Object Detection in AVs
#### 6.2.1 DL Approaches for Vehicle Detection  
A key element of ITS is vehicle identification, which is used in many applications such as traffic monitoring, ADAS, AVs, and traffic data. Robustness, acceleration, precision, and cost-effectiveness are the hallmarks of an intelligent system. For this, a variety of imaging sources are used, including satellite imagery, in-car cameras, UAV cameras, and traffic monitoring equipment. This study is mostly focused on on-road vehicle surveillance, wherein in-car cameras are essential. Nonetheless, there are a variety of difficulties in the field of vehicle detection. There are several different classifications of vehicles, each having a distinct size, color, structure, and design. Additionally, they can be viewed at a variety of scales and positions, frequently in intricate traffic scenarios. Adverse weather conditions create extra challenges since they distort visibility and add noise to the sensors, which can result in missed objects and false alarms. This section will examine the major research works and methodologies that have influenced DL approaches for vehicle detection under adverse weather conditions, offering a basis for the discussion of novel strategies and developments in this crucial domain. Table 4 summarizes the previous work on vehicle detection under adverse weather conditions. One paper [130] underscores the efficacy of CNNs in image processing through a tailored methodology for multiclass weather image categorization. Experimentation with various batch sizes (128, 256, 512, and 1024) is recommended to enhance accuracy and generalization. Testing on larger datasets enhances classifier accuracy and widens learning scope. In the realm of two-stage detectors, [PERSON]. [131] introduces a practical approach for vehicle detection in challenging conditions like fog, low light, and sun glare, leveraging three trained CNNs (ResNet, GoogleNet, and SqueezeNet) with transfer learning. Notably, ResNet achieves a validation accuracy of 100%, while GoogleNet and SqueezeNet achieve 65.50% and 90.0%, respectively. Additionally, [PERSON]. [132] employs Fast R-CNN for day and night vehicle detection, yielding impressive results under adverse conditions, validated by high recall (98.44%), accuracy (94.20%), and precision (90%). Furthermore, the on-road vehicle detection method proposed in [133] utilizes multiple region proposal networks (RPNs) in Faster R-CNN to identify vehicles across diverse sizes and weather scenarios, outperforming the existing techniques with high average precision rates on various datasets, including DAWN (89.48%), CDNet 2014 (91.20%), and LISA (95.16%). On the other hand, regarding one-stage detectors, another study [134] introduces an improved SSD-based front vehicle recognition algorithm for smart automobiles. Tested on the KITTI dataset, it achieves a high mAP of 92.18% and processes frames quickly at 15 ms per frame. The system enhances smart car safety in congested traffic and adverse weather conditions, prioritizing both accuracy and real-time performance. Another study [135] enhances a YOLO-based algorithm for precise vehicle detection in foggy weather by integrating a dehazing module with multi-scale retinex. This enhanced model, trained with augmented data, surpasses traditional YOLO in foggy conditions. Additionally, [PERSON]. [136] proposes a modified YOLO model for on-road vehicle detection and tracking across various weather conditions. Utilizing a single CNN, it exhibits robustness and outperforms the existing techniques in intelligent transportation systems. Furthermore, [PERSON] et al. [137] developed a nighttime vehicle detection method using fine-tuned YOLOv3, outperforming Faster R-CNN and SSD in terms of accuracy and efficiency. They achieved an average precision of 93.66%. Another study [138] utilized YOLOv4 with SPP-NET layers for vehicle detection, achieving an 81% mAP.