优化EfficientDet深度学习的车辆检测

针对深度学习EfficientDet模型的车辆检测性能进行分析,基于训练过程中容易陷入局部最优进行优化改进,构建分阶段自适应的训练模型,利用该训练模型对短距离和远距离车辆进行检测,并将检测结果与基于Cascade R?CNN和CenterNet方法进行比较,从计算复杂度、耗时及检测精度三方面分析显示本文方法优于其他两种...

Optimized EfficientDet deep learning model for vehicle detection

At present, deep learning has been widely applied in object detection, such as vehicle detection.In this paper, the deep learning EfficientDet model was analyzed, and its advantages in vehicle detection were confirmed.A phased adaptive training model was constructed to avoid local optimum in training process, then it was used to detect vehicles from both short and long distance.The detection results showed that compared with detection methods based on Cascade R-CNN and CenterNet, the proposed model was superior in terms of computational complexity, time consumption and detection accuracy.Meanwhile, further analysis figured out the optimal detection distance and angle.Finally, an example is given to verify that the proposed method can be applied to a large range of vehicle detection.