# Integration of Deep Learning Vision Systems in Collaborative Robotics for Real-Time Applications
## 3 Materials and Methods
### YOLO Versions  
YOLOv4 [32] improves accuracy and speed by introducing the CSPDarknet53 backbone, Mosaic Augmentation, and Self-Adversarial Training (SAT). These features enhance performance, especially for small objects, while keeping the model efficient. YOLOv5 [33] focuses on ease of use, being the first version implemented in PyTorch. It improves speed, supports larger image sizes, and introduces optimized loss functions and augmentation techniques, making it accessible for developers. YOLOv6 [34] targets industrial applications, optimizing performance for edge devices with the Efficient Rep Backbone (ERB) and neck. It offers better scalability for detecting objects at various sizes. YOLOv7 [35] balances speed and accuracy, introducing the Extended Efficient Layer Aggregation Network (E-ELAN) for better feature reuse. It surpasses previous versions in real-time object detection benchmarks, particularly for small and medium-sized objects. YOLOv8 [36] was developed by the same team that developed YOLOv5, featuring an optimized version of CSPDarknet53 as the backbone of the model. YOLOv8 introduces changes to the architecture, such as the \"C2f\" module, to deal with the detection of smaller objects, and a return to a model without anchors. It also introduces Complete Intersection over Union (CIoU) for bounding box loss and Decentralized Focal Loss (DFL) for classification and support for multiple backbone architectures, allowing flexibility of choice. Augmentation techniques such as CutMix and MixUp have been incorporated. YOLOv9 [37] builds on YOLOv8 with a focus on stability and scalability, making it suitable for complex scenarios that need high accuracy in object detection. It still uses Non-Maximum Suppression (NMS) for post-processing, which leads to slower inference times compared to YOLOv10 [38]. This version works best for tasks with many objects or smaller targets. YOLOv10, however, introduces a modular design with decoupled heads to improve detection at different scales and removes the need for NMS, cutting the post-processing time. It is the fastest version so far, with inference times as low as 49.1 ms, while maintaining accuracy [38]. YOLOv10 is ideal for real-time applications that need very fast processing, like videos or embedded systems. In short, YOLOv9 focuses on stability, while YOLOv10 is designed for speed and real-time use [39].  
Figure 5 shows the complete architecture of YOLOv10, with the base layers presented on the left of the image, the neck in the lower middle position, and the different decoupled head blocks at the right end of the diagram. The top right corner contains the organizationof the layers within the different layer modules, such as the C2f module, Spatial Pyramid Pooling Fast (SPPF), as well as the composition of the detection head.  
Since then, other new versions have been released, YOLOv9, YOLOv10, and YOLO11 [40], introducing architectural innovations with an improved CSPDarknet53 backbone and new Programmable Gradient Information (PGI) and Generalized Efficient Layer Aggregation Network (G-ELAN) components in the neck and slight improvements in average precision (mAP) for the MS COCO dataset.