###### Abstract  
This paper proposes an effective material delivery algorithm to address the challenges associated with Unmanned Aerial Vehicle (UAV) material transportation and delivery, which include complex route planning, low detection precision, and hardware limitations. This novel approach integrates the Whale-Swarm Hybrid Algorithm (WSHA) with the APCR-YOLOv8 model to enhance efficiency and accuracy. For path planning, the placement paths are transformed into a Generalized Traveling Salesman Problem (GTSP) to be able to compute solutions. The Whale Optimization Algorithm (WOA) is improved for balanced global and local searches, combined with an Artificial Bee Colony (ABC) Algorithm and adaptive weight adjustment to quick convergence and reduce path costs. For precise placement, the YOLOv8 model is first enhanced by adding the SimAM attention mechanism to the C2f module in the detection head, focusing on target features. Secondly, GhoHGNetv2 using GhostConv is the backbone of YOLOv8 to ensure accuracy while reducing model Params and FLOPs. Finally, a Lightweight Shared Convolutional Detection Head (LSCDHead) further reduces Params and FLOPs through shared convolution. Experimental results show that WSHA reduces path costs by 9.69% and narrows the gap between the best and worst paths by about 34.39%, compared to the Improved Whale Optimization Algorithm (IWOA). APCR-YOLOv8 reduces Params and FLOPs by 44.33% and 34.57%, respectively, with [EMAIL_ADDRESS] increasing from 88.5 to 92.4 and FPS reaching 151.3. This approach can satisfy the requirements for real-time responsiveness while effectively preventing missed, false, and duplicate detections during the inspection of emergency airdrop stations. In conclusion, combining bionic optimization algorithms and image processing significantly enhances the efficiency and precision of material placement in emergency management. U 2024 14 6621. 14 6621. 14 6621. 14 6621. 14 66  
the Particle Swarm Optimization (PSO) Algorithm [7], the Artificial Bee Colony (ABC) Algorithm [8], the Whale Optimization Algorithm (WOA) [9], and other intelligent algorithms are widely used in the computation of path planning because of their strong robustness, positive-feedback mechanism, and self-organization characteristics. [PERSON] et al. [10] proposed a PSO Algorithm based on migration learning, making it possible to find the optimal path for the TSP problem quickly. [PERSON] et al. [11] introduced a way to improve the starting population in the GA for UAV path planning in the target-coverage issue. This method accelerates the convergence of the GA and resolves collisions on the terrain surface. [PERSON] et al. [12] introduced a WOA using forward-looking sonar to address the challenge of planning three-dimensional paths for autonomous submersibles in the intricate undersea environment. This approach considers safety, smoothness, real-time constraints, and low integrity. In [13], a two-stage optimization algorithm based on bionic robotic fish is proposed to solve the path-planning problem, with currents and moving obstacles using staged optimization. Although many research results have effectively solved 2D or 3D path-planning problems, research for the Generalized Travelling Salesman Problem (GTSP) in military contexts is still insufficient. The path-planning problem in this paper is initially transformed into a computable GTSP. Considering the shortcomings of the WOA in terms of slow convergence speed and low solution accuracy, this paper proposes a Whale-Swarm Hybrid Algorithm (WSHA) based on the WOA's improvement, which combines the fine optimization ability of the ABC Algorithm in local search with the advantages of the Improved Whale Optimization Algorithm (IWOA) in global search. In addition, the convergence process is accelerated, and the total path cost is reduced by combining the 2-Opt Algorithm and the adaptive weight adjustment strategy [14].