Research on the Method and Application of Truck Type Recognition Based on Deep Learning from the Perspective of Unmanned Aerial Vehicles

A lightweight detection model for truck models based on improved YOLOv5s (MobileNetV3-YOLOv5s) is proposed to meet the requirement for real-time detection under the limited embedded device resources carried by drones. Firstly, we use MobileNetV3 to replace the backbone feature extraction network and use deep separable convolution to replace traditional convolution to reduce the model’s parameter count. Secondly, we use DIOU loss as the regression loss of the bounding box to enhance the convergence speed of the model and improve the ability to fit data. Finally, we use the K-means clustering method to reset the prior box. The experimental results show that the mAP value of the improved model is 89.6%, which is 0.2 percentage points lower than before, but the volume is only 3.98MB, which is about half of the original model. The detection speed is also significantly improved compared with before the improvement. Therefore, the lightweight model based on improved YOLOv5s improves detection speed and significantly reduces model volume while ensuring detection accuracy. It enables efficient, real-time recognition of truck models under complex road conditions on embedded devices.