Real-time Detection Algorithm of Aircraft Landing Gear based on improved YOLOv8

Abstract

This study innovatively improves the YOLOv8 target detection model, aiming to achieve fast and accurate detection of aircraft landing gear in natural environments. By introducing a small target detection layer, a dynamic serpentine convolutional layer, and a CoTattention mechanism, the study successfully optimized the original yolov8 model to effectively detect small-sized aircraft landing gears when presented at a distance. This paper introduces a small target detection layer of 160x160 on top of the original network, significantly improving the detection performance of airplane landing gear by fusing features from different layers. Dynamic serpentine convolution uses a dynamic structure and iterative strategy to improve the model's ability to perceive complex geometric structures by optimizing the convolution kernel. The CoTAttention mechanism allows the model to consider the information of each position in the input image more comprehensively. It significantly reduces the loss of contextual information by enhancing the ability to perceive small targets. The experimental findings demonstrate a noteworthy enhancement in the performance metrics, including precision, recall, and average accuracy, when comparing the enhanced model to its original counterpart. Furthermore, the improved model effectively meets the real-time detection requirements. Compared to other object detection models, the improved model performs, offering high accuracy and real-time detection capabilities, particularly demonstrating its versatility and practical value in detecting aircraft landing gear.