GMA-Net: A Lightweight Real-Time Network for Aero-Engine Damage Detection on Embedded IoT Devices

Borescope inspection for aero-engine damage is a non-destructive endoscopic technique based on IoT devices and optical means, and serves as an important measure to ensure flight safety. However, borescope images of aero-engines present multiple challenges, including various damage types, large scale variations, numerous small targets. These issues pose significant obstacles to improving the accuracy of intelligent damage detection. Especially when the algorithm needs to be deployed on embedded devices for real-time processing, it is necessary to achieve coordinated optimization of detection accuracy and processing speed. To address the above problems, this paper proposes an efficient lightweight aero-engine damage detection model named GMA-Net for real-time detection on embedded devices. First, the C3Ghost module is introduced to construct the backbone network, which significantly reduces redundant computation while enhancing the model’s feature expression capability and receptive field. Second, the lightweight SegNext Attention module is embedded, which strengthens the model’s perception of tiny damage features through a combination of strip convolutions. Finally, a novel detection head structure is designed. By integrating the Detect MBConv module with lightweight depthwise separable convolution and the attention mechanism, it improves the efficiency of feature capture for multi-scale damage targets. Experiments on the self-built aero-engine damage dataset demonstrate that GMA-Net achieves promising results. Compared with the state-of-the-art lightweight detector YOLOv13n, GMA-Net reduces the number of parameters by 46.5% and lowers the computational cost (GFLOPs) by 37.1%. It also achieves a 0.86% improvement in mAP@50–95 and a 7.17% increase in recall. In addition, the model reaches 22.19 FPS on the Jetson Nano embedded device, meeting the efficiency requirements of practical intelligent aero-engine damage detection. The source code related to this paper is available at: https://github.com/Evking-silver/GMA-Net.