## 4 Deep Learning-Based Object Detection Techniques for Machine Visions  
As widely recognized, machine vision plays a vital role in the overall development and deployment of autonomous vehicles, contributing to various aspects of perception, decision-making and control. Figure 12 below clarifies the highlights and presents the most significant content. Recent important contributions such as [74; 75] often involve advanced techniques such as computer vision, machine learning and sensor fusion. Researchers such as [76] presented a review of the perception systems and simulators for autonomous vehicles. Figure 11: The interaction between sensors and a controller in a machine vision system [73]. Figure 12: Section 4’s highlights: key points summarized. There is limited research in the field of autonomous forklifts, as seen in studies such as [77] (2020), [18] (2009), and [20] (2010), which indicate the early stages of research on this technology. Additionally, [11] focuses on vehicles handling wooden pallets, regardless of the type of load. Moreover, [74] also made a contribution, wherein the vehicle can handle loads of the same size and shape. This underscores the significance of this field. Figure 13 offers a comprehensive overview of the evolutionary landscape of deep learning methods for detection. Another significant advancement in object detection came with the introduction of You Only Look Once (YOLO), a method that divides images into a grid and simultaneously predicts bounding boxes and class probabilities for each region. The Single-Shot Multibox Detector (SSD) further enhanced real-time object detection by more effectively handling objects of varying shapes and sizes. The accompanying figure highlights these advancements, showcasing the remarkable contributions of YOLO, R-CNN and SSD in the continuous evolution of deep learning-based object detection techniques across a wide range of applications. Advanced learning methods have emerged as the cutting-edge approach for object detection, surpassing traditional computer vision techniques. These approaches leverage deep neural networks, specifically convolutional neural networks (CNNs), to autonomously capture and extract complex features from visual data. Object detection techniques based on deep learning not only attain impressive accuracy but also provide improved efficiency and speed. They have made substantial advancements in the realm of computer vision, facilitating diverse applications such as self-driving cars, surveillance systems and image identification. In this paragraph, we will explore some of the key advanced learning methods employed in object detection and their contributions to the field. Object detection can generally be categorized into two-stage and one-stage algorithms. Further detail will be provided below. Unfortunately, specifications on the precise machine vision model type utilized by each company is unattainable. This is due to several factors:  
* Companies often employ a hybrid approach, utilizing various models tailored to the specific tasks and demands of their autonomous forklifts. * Given the continuous evolution of models, companies may periodically update their systems with newer, enhanced iterations. * Detailed specifications concerning the models may be deemed proprietary information, thus not publicly disclosed. Nevertheless, overarching insights into the typical model categories utilized in autonomous forklifts can be provided. Object Detection: This is a function for autonomous forklifts, enabling the recognition and localization of items in their surroundings, such as pallets, obstacles and individuals. Prominent models for object detection that can be used for autonomous forklifts include YOLO, Faster R-CNN, Mask R-CNN, EfficientDet, Detection Transformer (DETR) and SSD. There are several popular algorithms and techniques used for object detection,  
Figure 13: Deep learning object detection methods and algorithms. such as convolutional neural networks (CNNs), and region-based convolutional neural networks (R-CNNs).