###### Abstract
Machine vision target detection research is also critical to ensure the accuracy of material airdrops in military emergency relief operations. Early deep-learning image processing models were mainly networks of the RCNN family, including R-CNN [15], Faster R-CNN [16], and so on. This two-stage model tends to be slow to detect, and thus challenging to utilize in industry. In 2016, [PERSON] et al. proposed a single-stage detection model YOLOv1 [17], and in the same year, this proposed single-stage algorithm also contained the SSD algorithm [18]. These two algorithms' accuracy is not as good as that of a two-stage algorithm model, such as the R-CNN, but the detection speed has been sufficient to meet the need for real-time detection. Nowadays, various scholars continue to improve on deep-learning algorithms, among which the target detection models with the most usage occasions include YOLOv3 [19], YOLOv5 [20], YOLOv7 [21], YOLOv8 [22], etc., which, thanks to the scholars' research on each model, make the deep-learning algorithms make breakthroughs in terms of the speed, accuracy, and performance of each of the params. [PERSON] et al. [23] improved the YOLO network to improve the accuracy and speed of target detection in aerial images. They solved the problems of low-positional accuracy, slow speed, target omission, and false detection of multiscale targets by clustering the target box dimensions, pre-training the network, multiscale training, and optimizing the screening rules. [PERSON] et al. [24] proposed Hybrid-YOLO using YOLOv5x and ResNet-18 classifiers, which provides a good classification of transmission line insulator defects by UAVs. [PERSON] et al. [25] used YOLO to extract features from ground-based thermal infrared (TIR) images and videos and applied it to target detection in TIR videos from UAVs. Although the techniques mentioned above somewhat enhance UAV target detection performance, the complexity and specificity of the UAV viewpoint image dataset, as well as the constraints of the UAV hardware platform, make it challenging to strike a balance between detection performance and hardware resource consumption. This paper introduces the APCR-YOLOv8 model as a solution to the challenges of detecting emergency airdrop stations under varying viewpoints, lighting conditions, and the need for high real-time performance. The model combines SimAM to improve target detection accuracy and utilizes the GhoHGNetv2 lightweight convolutional network to enhance model performance while reducing Params and FLOPs. In addition, a lightweight shared convolutional detection head (LSCDHead) is proposed for single-target detection tasks to reduce the parameters and FLOPs further and meet the real-time requirements. To better understand the methodologies used in this paper, it is essential to distinguish between artificial intelligence (AI) and metaheuristic algorithms (MHA). AI involves the simulation of human intelligence processes by machines and includes techniques such as machine learning and neural networks, which are often used for tasks like image recognition and decision-making. Conversely, MHA is a strategy designed to find suitable enough solutions for optimization problems by combining different heuristic methods, such as the GAs, PSO Algorithm, and WOA. In this paper, we leverage the strengths of both AI and MHA by integrating the WSHA with the APCR-YOLOv8 model. This combination achieves seamless integration of path planning and target detection to optimize UAV logistics and ensure precise delivery of supplies. The innovations of this paper are mainly in two aspects: (1) in path planning; by defining the problem as a GTSP, improving the spiral position update and convergence factor of the WOA, and combining the ABC Algorithm with the IWOA, we can efficiently solve the GTSP, significantly improving the path-planning efficiency and reducing the total flight distance.