Multiscale Infrared Object Detection Network Based on YOLO-MIR Algorithm
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摘要: 针对红外图像相对于可见光检测精度低，鲁棒性差的问题，提出了一种基于YOLO的多尺度红外图目标检测网络YOLO-MIR（YOLO for Multi-scale IR image）。首先，为了提高网络对红外图像的适应能力，改进了特征提取以及融合模块，使其保留更多的红外图像细节。其次，为增强对多尺度目标的检测能力，增大了融合网络的尺度，加强红外图像特征的进一步融合。最后，为增加网络的鲁棒性，设计了针对红外图像的数据增广算法。设置消融实验评估不同方法对网络性能的影响，结果表明在红外数据集下网络性能得到明显提升。与主流算法YOLOv7相比在参数量不变的条件下平均检测精度提升了3%，提高了网络对红外图像的适应能力，实现了对各尺度目标的精确检测。Abstract: To address the low detection accuracy and poor robustness of infrared images compared with visible images, a multiscale object detection network YOLO-MIR(YOLO for multiscale IR images) for infrared images is proposed. First, to increase the adaptability of the network to infrared images, the feature extraction and fusion modules were improved to retain more details in the infrared images. Second, the detection ability of multiscale objects is enhanced, the scale of the fusion network is increased, and the fusion of infrared image features is facilitated. Finally, a data augmentation algorithm for infrared images was designed to increase the network robustness. Ablation experiments were conducted to evaluate the impact of different methods on the network performance, and the results show that the network performance was significantly improved using the infrared dataset. Compared with the prevalent algorithm YOLOv7, the average detection accuracy of this algorithm was improved by 3%, the adaptive ability to infrared images was improved, and the accurate detection of targets at various scales was realized.
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Keywords:
- object detection /
- deep learning /
- infrared image /
- YOLO
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图 1 YOLO-MIR网络结构，Backbone负责特征提取，Neck负责特征融合，Head负责分类预测
Figure 1. YOLO-MIR network structure, Backbone is responsible for feature extraction, Neck is responsible for feature fusion, and Head is responsible for classification prediction.
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图 7 网络训练时的loss下降曲线；红色曲线(a)表示使用了本文提出的红外数据增广算法，蓝色曲线(b)表示使用传统数据处理方法
Figure 7. Loss descent curve in network training; The red curve (a) indicates the use of the infrared data augmentation algorithm proposed in this paper, and the blue curve (b) indicates the use of traditional methods
表 1 YOLOv7数据扩充方法在不同数据集上的对比
Table 1 Comparison of YOLOv7 data expansion methods on different data sets
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表 2 YOLO-MIR在FLIR数据集上的消融实验
Table 2 YOLO-MIR ablation experiments on FLIR dataset
YOLOv7 Avg pooling Data argument Multi-scale integration mAP50/% √ 90.0 √ √ 90.5 √ √ 90.9 √ √ 91.6 √ √ √ √ 92.7
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表 3 YOLO-MIR与其他网络在FLIR数据集上的对比实验
Table 3 Experiments comparing YOLO-MIR with other networks on FLIR dataset
Methods mAP/% Person/% Bicycle/% Car/% Parameters FLOPs/B Faster R-CNN 79.2 76.4 72.5 88.4 41.2M 156.1 YOLOv4 79.3 76.2 75.1 87.3 63.9M 128.3 YOLOv5m 81.6 78.0 78.1 89.2 35.7M 50.2 SMG-Y[19] 77.0 78.5 65.8 86.6 43.8M 54.7 PMBW[20] 77.3 81.2 64.0 86.5 36.0M 120.0 RGBT[21] 82.9 80.1 76.7 91.8 82.7M 130.0 YOLO-ACN 82.1 79.1 57.9 85.1 34.5M 111.5 YOLOv7 89.7 88.6 87.2 92.8 36.9M 104.7 YOLO-MIR 92.7 91.1 91.0 97.2 37.0M 104.8
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