Title: FCAODNet: a fast freight train image detection model based on embedded FCA

Authors: Longxin Zhang; Peng Zhou; Miao Wang; Chengkang Weng; Xiaojun Deng

Addresses: College of Computer, Hunan University of Technology, Zhuzhou, Hunan, China ' College of Computer, Hunan University of Technology, Zhuzhou, Hunan, China ' College of Computer, Hunan University of Technology, Zhuzhou, Hunan, China ' College of Computer, Hunan University of Technology, Zhuzhou, Hunan, China ' College of Computer, Hunan University of Technology, Zhuzhou, Hunan, China

Abstract: The fault detection of freight train image has some problems, such as low detection accuracy and slow detection speed. Aiming at the problem of slow detection speed in the process of train image fault detection, a lightweight object detection model fast channel attention network (FCAODNet) is proposed in this study. FCAODNet consists of four modules, including feature extraction network (FEN), lightweight multi-scale feature fusion (LMFF), prediction across scales (PAS), and decoding modules. FEN extracts image features, LMFF fuses features, PAS predicts the location of the target object, and the decoding module obtains the final prediction result. FCAODNet's FEN adopts CSPDarknet53tiny. The designed LMFF is embedded with two FCA modules to improve the detection accuracy. Experiments on train datasets and public datasets show that FCAODNet outperforms other state-of-the-art models in detection speed and has good detection accuracy and robustness.

Keywords: attention mechanism; fault detection; freight train; object detection.

DOI: 10.1504/IJCSE.2023.133692

International Journal of Computational Science and Engineering, 2023 Vol.26 No.5, pp.579 - 590

Received: 30 Aug 2022
Received in revised form: 13 Dec 2022
Accepted: 03 Jan 2023
Published online: 29 Sep 2023 *

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