PlaneNet: an efficient local feature extraction network

• Published in: PeerJ. Computer science Vol. 7; p. e783
• Main Authors: Lin, Bin, Su, Houcheng, Li, Danyang, Feng, Ao, Li, Hongxiang, Li, Jiao, Jiang, Kailin, Jiang, Hongbo, Gong, Xinyao, Liu, Tao
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 07.12.2021; PeerJ, Inc; PeerJ Inc
• Subjects: Accuracy; Algorithms and Analysis of Algorithms; Analysis; Artificial Intelligence; Artificial neural networks; Classification; Computer Vision; Computing time; Datasets; efficiency; Electronic devices; Feature extraction; Image segmentation; Local feature fusion; Mathematical models; Neural networks; Parameters; Reduce redundant; Semantic segmentation; Semantics; Semiconductor industry; Strong operability; United States; Feature extraction; efficiency; Local feature fusion; Strong operability; Reduce redundant
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.783

Due to memory and computing resources limitations, deploying convolutional neural networks on embedded and mobile devices is challenging. However, the redundant use of the 1 × 1 convolution in traditional light-weight networks, such as MobileNetV1, has increased the computing time. By utilizing the 1 × 1 convolution that plays a vital role in extracting local features more effectively, a new lightweight network, named PlaneNet, is introduced. PlaneNet can improve the accuracy and reduce the numbers of parameters and multiply-accumulate operations (Madds). Our model is evaluated on classification and semantic segmentation tasks. In the classification tasks, the CIFAR-10, Caltech-101, and ImageNet2012 datasets are used. In the semantic segmentation task, PlaneNet is tested on the VOC2012 datasets. The experimental results demonstrate that PlaneNet (74.48%) can obtain higher accuracy than MobileNetV3-Large (73.99%) and GhostNet (72.87%) and achieves state-of-the-art performance with fewer network parameters in both tasks. In addition, compared with the existing models, it has reached the practical application level on mobile devices. The code of PlaneNet on GitHub: https://github.com/LinB203/planenet .