## 5 Overview of Traditional and DL Approaches for Object Detection in AVs
### DL Approaches  
DL emerged as a subfield of machine learning and artificial intelligence in the 1990s [86]. Unlike traditional methods, DL offers distinct advantages, including the ability to achieve higher levels of abstraction, improved precision, and faster execution. These advantages make DL a valuable choice for OD. The OD algorithm employing DL typically consists of  
Figure 9: Primary structure of feature extraction for traditional and DL approaches. three core components: a recurrent neural network (RNN), a depth belief network (DBN), and a convolutional neural network (CNN). Overall, object detectors based on CNN [87] can be categorized into two primary types. 1. Single or one-stage detectors are known as the \"non-regional proposal method\" and \"dense prediction\". 2. A two-stage detector is known as the \"regional proposal method\" and \"sparse prediction,\" as shown in Figure 10. In a single-stage detector, all the tasks are integrated into a unified system structure. Conversely, a two-stage detector separates the system into distinct stages for region selection, classification, and localization. Some regional proposal techniques consist of region-based CNN (R-CNN) [88], Fast R-CNN [89], Faster R-CNN [90], Spatial Pyramid Pooling Networks (SPPNet) [91], and Feature Pyramid Network (FPN) [92]. On the other side, the non-regional category includes Single Shot Multibox Detector (SSD) [93], You Only Look Once (YOLO 1-8) [94], EfficientDet [95], DEtection TRansformer (DETR) [96], and Fully Convolutional One-Stage (FCOS) [97]. Since these methods constitute the foundation of CNN, they have emerged as the standard for OD. The amalgamation of one-stage and two-stage OD techniques has gained prominence in the field of OD within AVs. These hybrid approaches have demonstrated effectiveness across diverse scenarios, achieving promising results in precise OD and localization. Two-stage algorithms tend to deliver superior accuracy, while one-stage algorithms offer faster processing speeds. Notably, the reasons behind the lower accuracy of one-stage algorithms remain unclear. A study examining the drawbacks of one-stage algorithms, especially those with dense sampling, was conducted in Ref. [98]. The study found that there was a major problem with the unbalanced background samples (negative instances) and foreground values (positive examples). To solve this problem, the authors created Focal Loss, which alters the traditional cross-entropy loss function to give problematic cases more weight and thereby puts the emphasis on positive samples. After this adjustment, the RetinaNet network was created, showing better accuracy and quicker detection times than one- and two-stage methods. It should be mentioned too that the approach necessitates adjusting the focusing value, an extra hyperparameter [99]. However, the two primary state-of-the-art methods for DL-based detection these days are the YOLOv8 from the YOLO series (YOLOv1-YOLOv8) and the Faster R-CNN from the R-CNN family. Figure 10: Traditional and DL approaches. In order to speed up the region proposal step and allow the system to acquire knowledge of optimal region proposals, [PERSON] et al. [90] proposed Faster R-CNN as an improvement to Fast R-CNN. The architecture of Faster R-CNN is described in Figure 11. This was achieved mainly by integrating the region proposal network (RPN). RPN's main objective is to produce proposals with various ratios of aspects and sizes. Similar to Fast R-CNN, it employs high-resolution feature maps as input for RPN to identify the regions of interest in an image rather than depending on selective search for region recommendations. While Faster R-CNN improves the precision of detection and reaches an evaluation time of 0.2 s (five frames per second), it still requires a large amount of processing power and cannot fully meet the real-time system demands. It is also more complex because it requires training two interconnected neural networks, which makes system optimization difficult [100].