## 4 Deep Learning-Based Object Detection Techniques for Machine Visions
### Object Detection and Recognition Algorithms  
#### 4.1.1 Single-Stage Object Detectors  
Deep learning-based methods have revolutionized computer vision tasks like object detection. Among these, regression/classification-based techniques such as YOLO, SSD, DETR (Detection Transformer) and CornerNet-2019 have emerged as prominent solutions. CornerNet-2019 is an object detection model, as shown in Figure 14, that utilizes a single pass through a convolutional neural network to simultaneously identify object bounding boxes and their corresponding corners. This approach eliminates the need for multiple stages, making CornerNet a one-stage object detection method.  
The YOLO model is one of the most important algorithms in this field. YOLO, which stands for \"You Only Look Once\", is an object detection algorithm commonly used in computer vision and image processing tasks. It was launched in 2016 by [PERSON] et al. [79].  
As per [80], the mechanism details of YOLO by are clarified below: Each bounding box is predicted based on the entire image features. The algorithm predicts a bounding box for every class and detects whether objects are present throughout the image. In YOLO, the complete image is segmented into a grid of size SxS. Each grid cell is assigned a confidence value based on the predicted B bounding box. Object presence is determined by the confidence value. An object in a grid with a confidence value of zero is absent. In order to detect an object accurately, it is essential for the confidence score to closely match or equal the predicted bounding box and the real object. Probabilities associated with classes have also been anticipated via every grid cell. To identify objects within an image, we utilize a bounding box with class probabilities exceeding a specific confidence threshold. Ultralytics presents YOLO-v5 and YOLO-v8, with the latter demonstrating impressive real-time performance.  
Figure 15 illustrates the architecture. The latest addition to the YOLO family was released in February 2024. YOLOv9 was developed by a different group of developers, including [PERSON], [PERSON], and [PERSON], building upon the advances of its predecessors to deliver improved performance in object detection tasks [81]. Ultralytics released YOLO-v5 alongside YOLO-v8. While the formal paper release date is still pending, and additional features are yet to be incorporated into the YOLO-v8 repository, initial comparisons with its predecessors reveal its superiority, positioning it as the new state-of-the-art in the YOLO series [82].  
Figure 14: The architecture structure of CornerNet 2019 inspired by [78].  
The YOLO architecture is explained in Figure 15. The specific choice of technique depends on factors such as the requirements of the application, the available computational resources and the desired trade-off between accuracy and real-time performance. Moreover, RetinaNet, a one-stage object detection model, was introduced in the paper \"Focal Loss for Dense Object Detection\" by [PERSON], [PERSON], [PERSON] and [PERSON], which was presented at ICCV in 2017. Figure 16 illustrates the network architecture of RetinaNet. The one-stage object detectors such as SSD can be found in Figure 17.  
The structure of the below SSD model inspired by [83].