基于局部信息编码特征金字塔的轻量多类目标计数网络OA

Addressing the limitations of existing object counting algorithms,which struggle with background clutter and exhibit low accuracy when dealing with heavily occluded or significantly varying object scales,we propose a novel lightweight multi-class ob-ject counting network based on feature pyramid with local information encoding(FPLE-MOCN).This model leverages the redun-dancy of feature maps in convolutional neural networks to construct an efficient and rapid lightweight backbone network.Additional-ly,a feature pyramid module with a local information encoding mechanism is introduced to capture the local features of targets.Fi-nally,regression and classification heads composed of convolutional layers are employed for predicting the number and the location of objects at the same time.To achieve multi-class object counting,we combine the training sets of the existing crowd counting data-set(ShanghaiTech)and the vehicle counting dataset(CARPK)for training.For comparison with existing methods,we evaluate our model on the test sets of both datasets separately and use both mean absolute error and mean squared error as evaluation metrics for counting.Experimental results demonstrate that FPLE-MOCN can perform multi-class object counting and outperforms other meth-ods in terms of counting accuracy.