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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (4) : 22-32     DOI: 10.6046/zrzyyg.2022010
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A review and adaptability study of deep learning models for vehicle detection based on high-resolution remote sensing images
LYU Yanan1(), ZHU Hong1(), MENG Jian2, CUI Chengling3, SONG Qiqi1
1. College of Ecology Environment, Institute of Disaster Prevention, Langfang 065201, China
2. College of Earth Sciences, Institute of Disaster Prevention, Langfang 065201, China
3. Beijing Geoway Information-Technology Co., Ltd., Beijing 100043, China
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Abstract  

Vehicle detection is a hot research topic in the fields of computer vision, photogrammetry, and remote sensing. With the continuous development of deep learning technology, vehicle detection based on remote sensing images has been applied in fields such as smart city construction and intelligent transportation. This study systematically summarized existent vehicle detection algorithms based on remote sensing images and deep learning models and highlighted the classification, analysis, and comparison of one-stage and two-stage vehicle detection algorithms. Moreover, this study summarized the key technologies of vehicle detection in large-scale and complex backgrounds and analyzed the advantages and disadvantages of mainstream deep learning models of vehicle detection based on remote sensing images. Experiments were conducted to evaluate the YOLOv5, Faster-RCNN, FCOS, and SSD algorithms using DOTA and DIOR datasets. The vehicle detection precision based on the DOTA dataset was 0.695, 0.410, 0.370, and 0.251, respectively and that based on the DIOR dataset was 0.566, 0.243, 0.231, and 0.154, respectively. The experimental results show that the small target scale is still the main factor restricting the vehicle detection performance based on remote sensing images and that the application of deep learning models to the detection of small targets is to be further improved. Finally, based on public datasets and the analysis of existing algorithms, this study proposed the solution and development trend of vehicle detection based on remote sensing images in large-scale and complex backgrounds.
Keywords remote sensing image      vehicle detection      deep learning      analysis method     
ZTFLH:  P237  
Issue Date: 27 December 2022
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Yanan LYU
Hong ZHU
Jian MENG
Chengling CUI
Qiqi SONG
Cite this article:   
Yanan LYU,Hong ZHU,Jian MENG, et al. A review and adaptability study of deep learning models for vehicle detection based on high-resolution remote sensing images[J]. Remote Sensing for Natural Resources, 2022, 34(4): 22-32.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022010     OR     https://www.gtzyyg.com/EN/Y2022/V34/I4/22
模型 优点 缺点 检测精度
GoogLeNet[12] 引入超分辨率重建算法加强车辆特征信息 对密集目标检测效果差 自制遥感影像数据集上精度为0.75
AVPN+VALN[13] 提出VALN网络实现车辆方向信息检测,融合深浅层特征信息以提高小目标检测精度 网络结构复杂,训练检测时间成本较高 DLR[14]数据集上精度为0.92
ZF[15] 针对数据集自身特点,结合区域生成网络设置3种对应大小及比例的锚框,加快了检测速度 数据量小、来源单一,模型检测鲁棒性较差 自制遥感影像数据集上精度为0.88
DF-RCNN[16] 融合深浅层特征信息、引入可变形卷积和可变形感兴趣区域池化,改善密集区域小目标检测效果 网络计算量较大,检测耗时较长 自制Google Earth数据集上精度为
0.94
VGGNet[17] 网络层数可以随数据集图像大小进行调整 图像深层特征提取不充分,没有充分利用上下文语义信息 自制Google Earth数据集上精度为
0.89
VGG-16[18] 基于超像素分割提取道路区域进行车辆检测,缩小了检测范围 操作较为复杂,检测时间较长,对于非道路区域内车辆会造成漏检 DOTA[19]数据集上精度为0.73
SORCN[20] 加入道路区域分割提高检测精度 运行速度较慢,无法预测车辆方向信息,模型抗干扰性差,对于非道路区域内车辆无法检测 自制Google Earth数据集上精度为
0.96
Faster R-CNN++[21] 对遥感影像车辆进行多尺度融合及数据增强,提高了模型鲁棒性与小目标检测精度 检测速度较慢,无法实现车辆方向检测 DLR数据集上精度为0.57; Potsdam[22]数据集上精度为0.67; VEDAI[23]数据集上精度为0.458
Tab.1  Comparison of two-stage remote sensing image vehicle target detection
模型 优点 缺点 检测精度
YOLO[40] 基于K-means算法重新计算锚框大小,提高小目标检测精度 数据来源单一,难以应用于其他类型数据 自制Google Earth数据集上精度为
0.95
YOLT[41] 对遥感影像进行有重叠区域的裁剪,确保目标信息完整性 裁剪后的图像具有重叠区域,存在较多冗余计算 自制遥感影像数据集上精度为0.9
YOLOv3[42] 减少特征提取网络层数,增加精细输出特征图来检测小目标 数据集规模较小,鲁棒性较差 自制高分二号卫星影像数据集上精度为0.743
Oriented_SSD[43] 引入目标的角度偏移量,实现车辆角度信息预测 虽实现了角度信息的预测,但精度较低 DLR数据集上精度为0.86; VEDAI数据集上精度为0.8
Tab.2  Comparison of one-stage remote sensing image vehicle target detection
影像 数据集 目标
数量		 SSD结果 FCOS结果 Faster-RCNN结果 YOLOv5结果
影像1 DOTA 111
检测数量: 11 检测数量: 34 检测数量: 89 检测数量: 110
影像2 DIOR 86
检测数量: 0 检测数量: 0 检测数量: 8 检测数量: 27
Tab.3  Comparison of small scale vehicle target detection results
影像 数据集 目标
数量		 SSD结果 FCOS结果 Faster-RCNN结果 YOLOv5结果
影像3 DOTA 493
检测数量: 3 检测数量: 95 检测数量: 119 检测数量: 358
影像4 DIOR 327
检测数量: 0 检测数量: 6 检测数量: 33 检测数量: 268
Tab.4  Comparison of intensive small scale vehicle target detection results
影像 数据集 目标
数量		 SSD结果 FCOS结果 Faster-RCNN结果 YOLOv5结果
影像5 DOTA 68
检测数量: 48 检测数量: 61 检测数量: 61 检测数量: 68
影像6 DIOR 78
检测数量: 0 检测数量: 9 检测数量: 30 检测数量: 78
Tab.5  Comparison of multi-angle small scale vehicle target detection results
影像 数据集 目标
数量		 SSD结果 FCOS结果 Faster-RCNN结果 YOLOv5结果
影像7 DOTA 40
检测数量: 6 检测数量: 4 检测数量: 30 检测数量: 32
影像8 DIOR 83
检测数量: 7 检测数量: 27 检测数量: 33 检测数量: 60
Tab.6  Comparison of small scale vehicle target detection results under complex background
影像 数据集 目标
数量		 SSD结果 FCOS结果 Faster-RCNN结果 YOLOv5结果
影像9 DOTA 26
检测数量: 0 检测数量: 7 检测数量: 16 检测数量: 24
影像10 DIOR 33
检测数量: 0 检测数量: 0 检测数量: 13 检测数量: 17
Tab.7  Comparison of moving small scale vehicle target detection results
模型 输入大
小/像素		 DOTA DIOR
AP① 训练
时间/h		 AP 训练
时间/h
SSD 800×800 0.251 31.245 0.154 21.135
FCOS 800×800 0.370 54.510 0.231 63.367
Faster-RCNN 800×800 0.410 96.617 0.243 129.617
YOLOv5 800×800 0.695 43.333 0.566 38.915
Tab.8  Comparison of detection results of different algorithms in public data sets
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