ES-YOLO: a new lightweight fire detection model

Wang, Shi; Wang, Xiaohong

doi:10.1117/12.2660040

Cited by 4 publications

(3 citation statements)

References 15 publications

(19 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main research content of this paper is to combine the ES-YOLO [3] pedestrian detector with the StrongSORT pedestrian tracker with omni-scale feature fusion to obtain the proposed pedestrian multi-objective tracking model based on the TBD strategy. Finally, the effectiveness of this model is verified in the MOT16 dataset [4].…”

Section: Related Workmentioning

confidence: 99%

Optimization research on pedestrian multiobjects tracking model based on TBD strategy

Wang,

Liu,

Liu

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

View full text Add to dashboard Cite

The main task of pedestrian multi objects tracking technology is to continuously track multiple pedestrian objects simultaneously in video sequences and maintain their unique ID numbers. However, current pedestrian multi objects tracking models still have many problems, such as false detection, missed detection, and frequent ID number switching when pedestrians are obstructed or have overly similar appearances, ultimately leading to tracking failure. Therefore, this paper proposes a pedestrian multi objects tracking model based on TBD strategy. It mainly consists of two parts: pedestrian detector and pedestrian tracker. In terms of pedestrian detectors, this paper uses ES-YOLO pedestrian detectors. In terms of pedestrian trackers, this paper draws on the Omni-scale feature learning module in OSNet to redesign the StrongSORT pedestrian appearance feature extraction network, and ultimately obtains the StrongSORT pedestrian tracker based on omni-scale feature fusion, further enhancing its pedestrian feature extraction ability. In terms of experimental results. The experimental results of the pedestrian multi objects tracking model based on the TBD strategy in this paper on the MOT16 dataset show that the proposed pedestrian multi-objective tracking model can effectively improve the accuracy of pedestrian multi objects tracking and reduce the problem of frequent pedestrian ID number switching.

show abstract

Section: Related Workmentioning

confidence: 99%

Optimization research on pedestrian multiobjects tracking model based on TBD strategy

Wang,

Liu,

Liu

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

View full text Add to dashboard Cite

show abstract

“…Tsalera et al [13] enhanced the robustness and flexibility of the model using transfer learning, cross-dataset evaluation, and noise inclusion. Wang et al [14] proposed the YOLOv5s architecture, which incorporates the mix-up data enhancement strategy to address blurred target boundaries and adds a feature pyramid module to enhance image feature extraction. This model, referred to as Feature-Enhanced YOLO (FE-YOLO), achieved a mAP result of approximately 72.53%, representing an improvement of around 3.42% compared to the YOLOv5s network.…”

Section: Related Workmentioning

confidence: 99%

Sequential Learning of Flame Objects Sorted by Size for Early Fire Detection in Surveillance Videos

Samosir,

Nguyen,

Kong

2024

Electronics

View full text Add to dashboard Cite

This paper presents a sequential learning method aimed at improving the performance of a lightweight deep learning model used for detecting fires at an early stage in surveillance video streams. The proposed approach involves a sequence of supervised learning steps, wherein the entire training dataset is partitioned into multiple sub-datasets based on the size of fire objects. The size of fire objects is measured by object size ratio, which is the ratio of the bounding box area of the detected fire flame object relative to the entire image area. The initial training sub-dataset contains the largest-sized fire objects, progressing to the final sub-dataset containing the smallest-sized fire objects. The objective is to employ sequential learning to enhance the detection of small-sized fire objects relative to the image area using a lightweight model suitable for edge computing devices. Experiment results demonstrate that a deep learning fire detection model trained sequentially with a descending order of object size can effectively detect small flame objects with an object size ratio less than 0.006, achieving an F1 score of 93.1%, representing a 27% improvement compared to traditional supervised learning with no sequential learning steps. Additionally, performance in detecting tiny flame objects with an object size ratio less than 0.0016 achieves an F1 score of 94.5%, showing a 17.5% increase compared to the baseline without sequential learning.

show abstract

“…This makes it easy to analyze the location, shape, and size of fire or flood in the image. Some studies have used object detection networks to detect fires and forest fires [15][16][17]26]. However, although object detection can identify the location of the fire, as shown in Figure 2, it is difficult to analyze the size and shape of the fire accurately, so the detected area must be segmented to analyze the results.…”

Section: System Configurationmentioning

confidence: 99%

Semantic Segmentation Network Slimming and Edge Deployment for Real-Time Forest Fire or Flood Monitoring Systems Using Unmanned Aerial Vehicles

Lee,

Jung,

Suhr

2023

Electronics

View full text Add to dashboard Cite

In recent years, there has been a significant increase in the demand for unmanned aerial vehicle (UAV)-based monitoring systems to ensure proper emergency response during natural disasters such as wildfires, hurricanes, floods, and earthquakes. This paper proposes a real-time UAV monitoring system for responding to forest fires or floods. The proposed system consists of a hardware part and a software part. The hardware configuration is an embedded camera board mounted on the UAV, a Qualcomm QCS610 SoC with cores suitable for running deep learning-based algorithms. The software configuration is a deep learning-based semantic segmentation model for detecting fires or floods. To execute the model in real time on edge devices with limited resources, we used a network slimming technique which generates a lightweight model with reduced model size, number of parameters, and computational complexity. The performance of the proposed system was evaluated on the FLAME dataset consisting of forest fire images and the FloodNet dataset consisting of flood images. The experimental results showed that the mIoU of slimmed DeepLabV3+ for FLAME is 88.29%, and the inference speed is 10.92 fps. For FloodNet, the mIoU of the slimmed DeepLabV3+ is 94.15%, and the inference speed is 13.26 fps. These experimental results confirm that the proposed system is appropriate for accurate, low-power, real-time monitoring of forest fires and floods using UAVs.

show abstract

ES-YOLO: a new lightweight fire detection model

Cited by 4 publications

References 15 publications

Optimization research on pedestrian multiobjects tracking model based on TBD strategy

Optimization research on pedestrian multiobjects tracking model based on TBD strategy

Sequential Learning of Flame Objects Sorted by Size for Early Fire Detection in Surveillance Videos

Semantic Segmentation Network Slimming and Edge Deployment for Real-Time Forest Fire or Flood Monitoring Systems Using Unmanned Aerial Vehicles

Contact Info

Product

Resources

About