## 2 Related Work
### Real Time Object Detection Algorithms  
With the emergence of deep learning algorithms, the YOLO model was proposed for real-time applications. The success of the YOLO model in real-time applications directs researchers to this field. The first YOLO models (YOLOv1-YOLOv3) [3; 5; 31] were single-stage detection models consisting of the following three parts: the backbone, neck, and head. These try to predict multidimensional objects by branching within the sections. The deep attention-based YOLOv3 model [32] improved the accuracy of small object detection by incorporating attention mechanisms.  
YOLOv4 [6] uses the Darknet structure and optimizes the backbone layer. Using the Mish activation function as the activation function, YOLOv4 has achieved improved results in data transmission methods with the Path Aggregation Network (PAN). Today's YOLO algorithms are based on the YOLOv4 core architecture.  
YOLOv5 and YOLOv8 [33; 34] are new versions of YOLO based on the YOLOv4 model structure, with an improved data augmentation strategy and more model types. In object detection, to overcome the conflict between classification and regression tasks, YOLOv uses a decoupled head architecture [35; 36]. YOLOX creates a new paradigm for the YOLO model design by incorporating multiple positives, decoupled head, into the model structure. YOLOv6 [20; 37] introduced the reparameterization method to the YOLO series models for the first time by proposing the EfficientRep Backbone and Rep-PAN Neck. YOLOv7 [21] focuses on analyzing the impact of gradient paths on model performance and proposes the E-ELAN structure to improve model capacity without disrupting the original gradient paths. YOLOv8 [38] takes the strengths of previous YOLO models and integrates them into the SOTA of the existing YOLO family.  
In recent years, researchers have extensively explored the potential of YOLO architectures for domain-specific applications, demonstrating the versatility of this framework. Some notable advancements include the following:  
* Integration of a dehazing algorithm with YOLOv10 for ship fire detection [39]: A recent study integrated a dehazing algorithm with YOLOv10 to improve ship fire detection under challenging atmospheric conditions such as haze or fog. By preprocessing images to enhance visibility, the modified YOLO model achieved superior accuracy and robustness in maritime environments. This demonstrates how predetection enhancements can significantly boost model performance in specific use cases, particularly where environmental factors impair visibility.
* Gold-YOLO--efficient object detector with a collect-and-scatter mechanism: Gold-YOLO introduces an innovative collect-and-scatter mechanism to efficiently aggregate and redistribute feature information, achieving improved accuracy while reducing computational overhead. This adaptation is particularly effective for real-time applications requiring high-speed processing [40].
* YOLO-WDNet--a lightweight and accurate model for weed detection in cotton fields: YOLO-WDNet focuses on resource-constrained agricultural environments, utilizing a lightweight architecture to maintain high detection accuracy for weed identification in cotton fields. This demonstrates the importance of optimizing YOLO models for specific environmental and computational constraints [41].
* Enhanced drone-based weed detection using a feature-enriched deep learning approach: In this work, the researchers enhance weed detection accuracy through feature enrichment modules that capture fine-grained details. This approach proves effective for the drone-based detection of small and subtle objects in agricultural settings, underlining the significance of feature optimization in YOLO-based architectures [42].