Izvorni znanstveni članak

https://doi.org/10.17559/TV-20230210000345

A Lightweight Convolutional Neural Network for Salient Object Detection

Fengchang Fei ; College of Modern Economics and Management, Jiangxi University of Finance and Economics, Nanchang, China, 330032
Wei Liu ; Lenovo Research, Shenzhen, China, 518057
Lei Shu ; School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China, 330032 *

* Autor za dopisivanje.

Sažetak

U-shape networks are widely used in salient object detection. Recently, CTDNet with a Comprehensive Triangular Decoder improved detection efficiency, which made some improvement with respect to the complexity and slow training of U-shape networks. However, CTDNet is still not lightweight enough, and the use of Global Average Pooling for top-level semantic features can lead to the loss of global structural information. This paper proposes Trilateral Enhanced Network (TENet), a faster salient detection model based on CTDNet, for industrial application. TENet uses MobileNetV3 as a backbone network so that TENet only needs 3.72M parameters, which lightweight the network consequently. TENet contains a feature fusion module called Channel Attraction Enhanced Feature Fusion Model, which integrates high-level semantics to improve accuracy. Additionally, Convolutional Block Feature Enhancement Module is proposed, which can further enhance accuracy. In comparison with CTDNet, TENet is a lightweight network with faster detection speed and more detection accuracy. TENet robustly detects defects in salient texture images, indicating insensitivity to texture interference. Experiments show TENet maintains strong performance on salient textures detection, demonstrating suitability for industrial optical inspection.

Ključne riječi

CTDNet; industrial optical inspection; MobileNetV3; salient object detection; TENet

Hrčak ID:

318502

URI

https://hrcak.srce.hr/318502

Datum izdavanja:

27.6.2024.

Posjeta: 25 *