基于关键点检测的无锚框轻量级目标检测算法

Lightweight Anchor-free Object Detection Algorithm Based on Keypoint Detection

针对基于关键点的目标检测参数量大、检测框误匹配的问题,提出一种轻量级的基于关键点检测的无锚框目标检测算法。首先将输入图片输入优化过的特征提取算法,通过级联角池化与中心池化,输出3个关键点的热力图与它们的嵌入向量;然后通过嵌入向量匹配热力图并画出检测框。文中的创新点在于将SqueezeNet中的轻量级模块firemodule适配至CenterNet,并用深度可分离卷积代替主干网的常规卷积,同时,针对CenterNet的检测框误匹配问题优化了算法输出形式与训练时的损失函数。实验结果表明,改良后的算法使得原有的CenterNet算法模型尺寸缩小为原来的1/7,同时检测精度与速度较YOLOv3,CornerNet-Lite等相同量级的算法仍有所提高。

According to the large number of parameters of key-point object detection network and the problem of mismatching of bounding box,this paper proposes a lightweight key point anchor-free object detection algorithm.It inputs the image into the improved hourglass network to extract features,through the cascade corner pooling module and center pooling module,outputs three key points heatmap and their embedding vectors.At last,it matchs the key points by embedding vectors and draw the bounding box.The innovation of this paper is to applying the firemodule of SqueezeNet in the CenterNet object detection network,and replace the conventional convolution in the backbone with the depth separable convolution.At the same time,aiming at the mismatching bounding box problem in CenterNet,this algorithm adjusts the network’s output and loss function.Experiment results show that the model size is reduced to 1/7 of CenterNet,while the accuracy and inference speed are still higher than the same size target detection algorithm like YOLOv3 and CornerNet-Lite.