Vehicle-based Thermal Imaging Target Detection Method Based on Enhanced Lightweight Network
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摘要: 车载热成像系统不依赖光源，对天气状况不敏感，探测距离远，对夜间行车有很大辅助作用，热成像自动目标检测对夜间智能驾驶具有重要意义。车载热成像系统所采集的红外图像相比可见光图像具有分辨率低，远距离小目标细节模糊的特点，且热成像目标检测方法需考虑车辆移动速度所要求的算法实时性以及车载嵌入式平台的计算能力。针对以上问题，本文提出了一种针对热成像系统的增强型轻量级红外目标检测网络（Infrared YOLO，I-YOLO），该网络采用（Tiny you only look once，Tiny-YOLO V3）的基础结构，根据红外图像特点，提取浅层卷积层特征，提高红外小目标检测能力，使用单通道卷积核，降低运算量，检测部分使用基于CenterNet结构的检测方式以降低误检测率，提高检测速度。经实际测试，Enhanced Tiny-YOLO目标检测网络在热成像目标检测方面，平均检测率可达91%，检测平均速度达到81Fps，训练模型权重96MB，适宜于车载嵌入式系统上部署。
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关键词:
- 车载热成像系统 /
- 夜间智能驾驶 /
- I-YOLO红外目标检测网络 /
- CenterNet结构 /
- 车载嵌入式平台
Abstract: A vehicle-based thermal imaging system does not depend on a light source, is insensitive to weather, and has a long detection distance. Automatic target detection using vehicle-based thermal imaging is of great significance for intelligent night driving. Compared with visible images, the infrared images acquired by a vehicle-based thermal imaging system based on existing algorithms have low resolution, and the details of small long-range targets are blurred. Moreover, the real-time algorithm performance required to address the vehicle speed and computing ability of the vehicle-embedded platform should be considered in the vehicle-based thermal imaging target detection method. To solve these problems, an enhanced lightweight infrared target detection network (I-YOLO) for a vehicle-based thermal imaging system is proposed in this study. The network uses a tiny you only look once(Tiny-YOLOV3) infrastructure to extract shallow convolution-layer features according to the characteristics of infrared images to improve the detection of small infrared targets. A single-channel convolutional core was used to reduce the amount of computation. A detection method based on a CenterNet structure is used to reduce the false detection rate and improve the detection speed. The actual test shows that the average detection rate of the I-YOLO target detection network in vehicle-based thermal imaging target detection reached 91%, while the average detection speed was81 fps, and the weight of the training model was96MB, which is suitable for deployment on a vehicle-based embedded system. -
表 1 4类检测目标统计数据分析
Table 1. Statistical analysis of four kinds of detection targets
Detection model Mp(%) Mf(%) Mm(%) Person Car Bus Truck Person Car Bus Truck Person Car Bus Truck SSD300×300 66 71 73 68 12 13 14 11 21 12 21 20 RetinaNet-50-500 90 89 88 92 15 17 18 14 6 4 6 14 Tiny-YOLOV3 65 70 75 69 15 10 15 10 20 15 23 21 YOLOV3 95 90 90 95 20 18 20 15 5 3 5 15 I-YOLO 91 88 89 93 3 5 3 5 9 8 6 18 
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表 2 综合性能测试对比分析
Table 2. Comparison and analysis of comprehensive performance tests
Detection model Mp/% Mf/% Mm/% Mo/FPS Mw/MB SSD300×300 67 11 31 13 196 RetinaNet-50-500 90 15 13 7 246 Tiny-YOLOV3 66 12 32 62 34 YOLOV3 95 16 6 21 234 I-YOLO 91 6 12 81 96
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