## 2 Dataset Description and Applied Algorithms
### Applied Algorithms
As neural networks deepen, they encounter problems that require the auxiliary reversible branch to be developed. An information bottleneck will cause network deepening, resulting in unreliable gradients produced by the loss function. It is designed to handle the error accumulation problem caused by multiple prediction branches and the lightweight architecture caused by deep supervision. Figure 6: YOLOv7 model architecture [35]. Figure 7: Methods and architectures for PGI networks. (**a**) Path Aggregation Network (PANN)) [39], (**b**) Reversible Columns (RevCol) [40], (**c**) conventional deep supervision, and (**d**) Programmable Gradient Information (PGI) [38]. GELAN's architecture is given in Figure 8. Enhancing Temporal Action Detection with Location Awareness (ELAN), which provides accurate and efficient detection of actions in videos, and Cross Stage Partial Network (CSPNet), which increases the efficiency and accuracy of convolutional neural networks, are combined to form GELAN. GELAN combines the strengths of these two architectures. **Gray Wolf Optimizer (GWO):** Based on grey wolf tactics and social behaviors, GWO is a metaheuristic optimization method. A balance between exploring the whole solution space and focusing on local regions is achieved by this algorithm by using alpha, beta, delta, and omega wolves to represent potential solutions within the search space [43]. Wolf positions are dynamically adapted in this process toward potential solutions, following the movement of predators tracking prey. The wolf positions are refined iteratively by the algorithm, leading to the optimal solution being reached with each iteration. Using these techniques, the GWO algorithm gradually narrows down the search space to find the most optimal solution. As a result, it is useful for optimizing a wide range of tasks. The iterative process of emulating the natural social hierarchy and hunting tactics of grey wolves within the GWO algorithm for tackling optimization problems is illustrated in the flowchart in Figure 9. During each iteration, candidate solutions, symbolized as wolf positions in the search space, undergo updates influenced by three primary roles: alpha, beta, and delta wolves, with represent the best, second-best, and third-best solutions identified thus far. **Artificial Rabbit Optimizer (ARO):** The ARO was released as a new meta-heuristic optimization algorithm in 2022 [29]. Various optimization algorithms inspired by nature are developed to find the best solution. In ARO, the process of foraging and exploring the environment of rabbits is modeled and simulated in this way. Rabbits, by nature, avoid dangerous environments and at the same time explore new environments to find the best food source. Similarly, ARO provides an algorithm where optimal solutions can exist by discovering new search areas. In addition to machine learning, ARO has been applied for optimization in various fields such as finance, engineering, and logistics. ARO offers a method that effectively performs optimization operations, inspired by rabbits searching for food in nature. Figure 10 shows the flowchart showing how the ARO algorithm works. The position and control parameters of the rabbits are loaded. The energy factor of the rabbits is calculated. As the number of iterations with a random oscillator increases, the extent of the energy factor A determines their search behavior. If A is greater than 1, a rabbit is randomly selected, and a foraging maneuver is performed. If A is less than 1, a tunnel is created, a random hiding place is chosen, and a random hiding maneuver is performed. After one of the two states is realized, the fitness is calculated, and the position is updated. The best solution was found until this state was updated. These steps are repeated until the stopping criterion is met. Finally, the best solution is returned. Figure 8: The architecture of GELAN: (**a**) CSPNet [41], (**b**) ELAN [42], and (**c**) GELAN [38].