**1. Introduction**

Vehicles are necessities in human life and are extensively utilized in logistics, transportation and travel. The termination of the production of traditional internal combustion engine vehicles (ICEVs) is being gradually implemented worldwide under the pressure of the global energy shortage and environment pollution issues, and electric vehicles (EVs) are recognized ideal alternatives in this situation. Partially or fully driven by Li-ion batteries, EVs have presented the potential hazard of fire, which heavily affects the safety of passengers under various scenarios, e.g., parking, charging and driving. Fire incidents in EVs and plug-in hybrid electric vehicles (PHEVs) mostly begin in the battery power system. Compared with gasoline-caused vehicle fires, battery-caused vehicle fires contain more energy, extremely high temperatures, and the release of combustible and toxic gas, thus leading to higher risks and difficulty in extinguishing the fire [1,2].

In order to eliminate potential fire hazards and improve the manufacturing safety of EVs, correlative research should not only focus on prevention of combustion, but also on analysis and research of existing cases of burnt EVs. Recently, the on-spot investigation of burnt EVs has become an important method for analysis and research. Fire or damage traces remaining on the body panels and vehicle frames are frequently used to locate the origin of fire [3]. When the vehicle is not burnt extensively, traces with salient appearances, e.g., burnt-off paint and rusted metal, can provide reliable clues for the determination of fire origin [4]. Due to the similarity of material and paint utilized in EVs and conventional vehicles, fire traces of bodies of burnt EVs are also applicable and credible for investigation. Moreover, fire traces can be conveniently captured as digital images, which also provides possibilities for using a computer vision method for recognition.

**Citation:** Pu, J.; Zhang, W. Electric Vehicle Fire Trace Recognition Based on Multi-Task Semantic Segmentation. *Electronics* **2022**, *11*, 1738. https://doi.org/10.3390/ electronics11111738

Academic Editors: Luis Hernández-Callejo, Sergio Nesmachnow and Sara Gallardo Saavedra

Received: 5 May 2022 Accepted: 27 May 2022 Published: 30 May 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Semantic segmentation is one of the major computer vision tasks that applies endto-end classification of every pixel of the image input and outputs a corresponding segmentation map, in which a cluster of pixels classified as the same class is called semantic. With fully convolutional network (FCN) [5] first introduce convolutional neural network (CNN) into semantic segmentation, multiple advanced network structures with various optimization methods were proposed, e.g., contextual information-reinforced PSPNet [6] and DeepLab [7,8] and attention mechanism-based DANet [9] and PSANet [10]. Multiple backbones are also implemented in semantic segmentation tasks for different purposes, e.g., ResNet [11,12] with deep architecture, MobileNet [13] as a lightweight framework, and HRNet [14] for high-resolution feature extraction.

With the improvement of computer performance and the emergence of in-depth research on deep learning, semantic segmentation has been utilized in various practical tasks and has achieved par excellence performance. In the medical field, Ronneberger et al. [15] proposed U-Net with an encoder-decoder architecture for biomedical segmentation tasks. Milletari et al. [16] proposed a variant called V-Net that utilized residual blocks. Zhou et al. [17] proposed a much more complex UNet++ with sub-networks connected through a series of nested, dense skip pathways. Apart from the structures, the targets for medical segmentation also varies, e.g., lungs, lesions, lobes, tumours, and vessels. In the scene parsing and automatic driving field, Zhao et al. [6] proposed PSPNet with a classic pyramid pooling module. Charles et al. [18] expanded the input of the network to 3d point sets and proposed a related structure named PointNet. Kirillov et al. [19] combined sematic segmentation and instance segmentation tasks and proposed a new task called panoptic segmentation. Semantic segmentation is also in large-scale use for fire and smoke detection and recognition. Wang et al. [20] proposed a model concentrated on small fire and smoke regions in video data. Zhang et al. [21] proposed a lightweight U-Net-based network for forest fire detection and recognition. Mseddi et al. [22] proposed a method combining YOLOV5 and U-Net for fire detection and segmentation. Moreover, in the remote sensing field, Chen et al. [23] proposed symmetrical dense-shortcut frameworks for very-high-resolution images, and Zhang et al. [24] proposed a dual lightweight attention network for high-resolution remote sensing images.

Currently, no semantic segmentation-based research on the recognition of EV fire traces has been implemented, and no corresponding dataset has been built for the task. However, according to the forementioned analogous tasks, semantic segmentation would be compatible with the EV fire trace recognition task of this paper. The combination of semantic segmentation would not only output a preliminary analytical result of burnt EVs by collecting images conveniently, but also make its output a status description of burnt EVs for further archive and research. In summary, the main contributions of this paper can be summarized as follows:


The proposed model and an executable demo are available in Supplementary Materials at: https://github.com/Jkreat/EVFTR (accessed on 27 May 2022).
