## 6 Traditional and DL Approaches for Object Detection in AVs
### Traditional Approaches for Object Detection in AVs
#### 6.1.1 Traditional Approaches for Vehicle Detection  
Traditional vehicle detection techniques, which rely on appearance- and motion-based algorithms, have drawbacks, including being sensitive to changes in the camera's position, having trouble telling moving objects apart, and requiring a large number of photos to accurately identify motion. Appearance-based algorithms that make use of features, such as HOG, Haar-Like, and SIFT, can improve identification, but they are vulnerable  
Figure 12: Structure of YOLOv8. to exterior objects, adverse weather, and lighting variations. Even though they help to identify objects, biased classifiers like SVM and ANN might perform poorly in scenarios involving complicated traffic, which can result in false positives and make them susceptible to changes in the background, obstacles, lighting, and dimensions. One study [101] investigated issues with physical detection techniques and equipment maintenance by examining the application of SVM and Pyramid Histogram of Words (PHOW) for traffic and road condition detection. The experimental findings show an accuracy of more than 82.5% on average under different weather circumstances. The suggested technique efficiently divides photos into four categories based on the type of traffic and road conditions. However, the paper does not detail the dataset's diversity, which could impact the model's robustness and generalizability. The research in [102] tackled the requirement for a reliable traffic surveillance system that takes into consideration variables like headlights, reflections, and shadows and operates well in a variety of weather and lighting circumstances. The suggested method is based on pattern classification and uses a distinctive image's histogram as a distinguishing characteristic for classification. Because of its excellent generalization skills, SVM is used as a classifier. In tests, the system can discriminate between headlights and shadows with good accuracy and recognize moving cars in a variety of weather and lighting scenarios. However, this paper does not address the system's performance under high-density traffic or its computational efficiency, which could be critical limitations for real-time applications. Using webcams as weather sensors, researchers [85] came up with a novel technique that allowed them to identify over 180,000 annotated photographs depending on indoor or outdoor circumstances with a 98% accuracy rate using SVM. The results of the research [103] indicate that DPM effectively addresses occlusion difficulties, even though it might not be required for ordinary detection settings. Accuracy is improved by adding image context information, and system speed is increased by adding other features. On the other hand, the multi-scale system only slightly improves accuracy, and the classifier has little effect on detector quality [21; 104; 105]. The vehicle detection method in [106], which utilizes pseudo-visual pursuit and HOG-LBP feature fusion, showcases the superiority of traditional machine learning techniques in achieving high detection accuracy and speed in severe weather conditions. This approach, with its reliance on radar information, demonstrates a clear advantage over methods that do not incorporate such advanced sensor data, particularly in scenarios where visibility is compromised. In contrast, the work by [PERSON] et al. [107], while also effective, highlights the limitations of traditional methods when solely relying on MOG 2 background subtraction and histogram equalization. Their system's high effectiveness rate of 96.4% across various weather conditions suggests that traditional techniques can still perform well. The vehicle detection system by [PERSON] and [PERSON] [108], which uses histogram extension and the Gray-level Differential Value Method (GDVM), shows that traditional methods can be effective in complex traffic conditions.