轻量化自适应特征选择目标检测网络

在小目标物体检测、多类别物体检测尤其是轻量化检测模型等关键技术研究方面仍面临较大的挑战,基于此,本文提出一种轻量化自适应特征选择目标检测网络.该网络以特征金字塔为基础,提取多尺度图像特征并从空间维度上对特征图进行滤波,从通道维度上自适应地选择特征图中更重要的通道,降低多通道下噪声和干扰对目标特征的稀释作用,减少特征图在...

Lightweight Adaptive Feature Selection Network for Object Detection

There are some limitations of key technologies in small object detection， multi-category object detection， and especially lightweight models. To solve these problems， this paper proposes a lightweight adaptive feature selection network for object detection. The network is constructed to extract multi-scale features based on the feature pyramid. To alleviate the noise interference and preserve the detail information， a feature selection module composed of spatial adaptation and channel adaptation is designed. Specifically， the feature maps are filtered from spatial dimension， encoded and decoded in channel dimension for selecting the meaningful features adaptively. Besides， the classification network is constructed through depth-wise separable convolution to reduce the computational cost， improve detection efficiency and realize the lightweight version of the network. The detection accuracy on the PASCAL VOC 2007 dataset is 77.7% in mean average precision(mAP)， and the detection speed is 14.3 in frames per second(FPS). The results on the MS COCO dataset show that the proposed network outperforms FPN and Mask R-CNN at the cost of 5% accuracy loss and achieves a better balance between accuracy and efficiency.