Real-time lightweight drone detection model: Fine-grained Identification of four types of drones based on an improved Yolov7 model

Abstract

Recently, the rapid progress of artificial intelligence has enhanced the human-robot relationship through the development of several autonomous robots; such as drones. The overwhelming rise of drones has brought both relevant advantages and threatening risks into our daily lives. They are strongly deployed to achieve hard tasks and reach critical areas within a short period of time. In the past few years, drones' anarchic and malicious use has generated many incidents and accidents, causing far-reaching impacts. Due to their dual-edged nature, they are regarded as potential threats. Recognizing the type of flying drones represents a challenging task for anti-drone systems to reinforce airspace safety and security. To this end, we propose a novel lightweight model able to differentiate between the main types of drones in real-time using appropriate modules. To find the optimal compromise between size, performance and speed, a lightweight model is proposed based on Yolov7 with the incorporation of CNeB and C3C2 modules as well as the Re- Parameterization Decoupled (RePD) head structure. Further, a CNeB module is used to reduce the model’s size and improve the model’s performance, a C3C2 module to enhance the feature extraction and fusion and a Re- Parameterization Decoupled RePD to improve both the efficacy and accuracy of the prediction part while reducing the inference time. The experimental results show that the Yolov7-CNeB-C3C2-RePD meets the anti-drone requirements by improving the detection performance, minimizing the inference speed and reducing the model size. In comparison to other models, the developed has provided high results reaching 91.9% precision, 90.5% recall, 95% mAP@0.5, and 20.3 ms during the inference stage, which outperforms the other algorithms in the benchmark. Furthermore, the model has an average detection speed of 0.003 ms per image and a capacity to process 27.88 frames per second, thus satisfying the real-time requirements.