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摘要:
视频图像中的小像素目标难以检测。针对城市道路视频中的小像素目标，本文提出了一种改进YOLOv3的卷积神经网络Road_Net检测方法。首先，基于改进的YOLOv3，设计了一种新的卷积神经网络Road_Net；其次，针对小像素目标检测更依赖于浅层特征，采用了4个尺度检测方法。最后，结合改进的M-Softer-NMS算法来进一步提高图像中目标的检测精度。为了验证所提出算法的有效性，本文收集并标注了用于城市道路小像素目标物体检测的数据集Road-garbage Dataset，实验结果表明，本文算法能有效地检测出诸如纸屑、石块等在视频中相对于路面的较小像素目标。
Abstract:Small pixel targets in video images are difficult to detect. Aiming at the small pixel target in urban road video, this paper proposed a novel detection method named Road_Net based on the YOLOv3 convolutional neural network. Firstly, based on the improved YOLOv3, a new convolutional neural network Road_Net is designed. Secondly, for small pixel target detection depending on shallow level features, a detection method of 4 scales is adopted. Finally, combined with the improved M-Softer-NMS algorithm, it gets higher detection accuracy of the target in the image. In order to verify the effectiveness of the proposed algorithm, this paper collects and labels the data set named Road-garbage Dataset for small pixel target object detection on urban roads. The experimental results show that the algorithm can effectively detect objects such as paper scraps and stones, which are smaller pixel targets in the video relative to the road surface.
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Key words:
- video image /
- smaller pixel object /
- convolutional neural network
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Overview: Small pixel target detection is a kind of difficult program. Existing object detection benchmarks and methods mainly focus on standard detection task. However, these ways cannot get good performance on low-pixel ratio object detection, which has a few pixel in high resolution images. And the early target detection frameworks such as R-CNN, YOLO series are not very good for small pixel target detection. In order to solve this problem, this paper proposes an improved YOLOv3 network and the algorithm using M-Softer-NMS to improve the detection ability of small targets. Firstly, Road_Net convolutional neural network is proposed. YOLOv3's Darknet53 network is too complicated and redundant. What's more, too many parameters will bring difficulty in training, increase the requirements on the dataset, and reduce the speed of detection, which will not achieve better real-time performance. Accuracy and real-time performance are challenging in small object detection on urban roads. Therefore, we proposed a convolutional neural network Road_Net with relatively low computational complexity as a feature extraction network. Secondly, a detection method of 4 scales is used to more fully use shallow level features. In view of the fact that the targets in this context are mostly small pixel targets, the original three scale detections are extended to four scale detections, and the larger feature maps are assigned to the smaller pixel targets with more accurate anchor frames. Finally, M-Softer-NMS algorithm is used to further improve the detection accuracy of the target in the image. Softer-NMS is further improved after Soft-NMS. A new loss function (KL Loss) for bounding box regression is proposed to learn the bounding box transformation and positional reliability at the same time. Combined with the characteristics of small pixel targets in this paper, the M-softer-NMS algorithm for this paper is proposed based on softer-NMS. In order to verify the effectiveness of the algorithm, we collected and labeled the data set named Road-garbage Dataset for the detection of small pixel target objects on the road. The Dataset is based on several main roads in a certain city and selects 1200 different main roads in different regions. The experimental results show that the accuracy, recall rate and AP can reach 95.29%, 91.12% and 82.41% respectively, while real-time detection is 57.9 f/s. In the next work, we will continue to improve the network and optimize the algorithm for higher accuracy and lower time cost, and continue to capture and use our more realistic scene images to expand our dataset for better application.
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表 算法1：M-Softer-NMS
Input: B={b1, .., bN }, S={s1, .., sN }, C={σ12, .., σ2N }, Nt Output : D，S 1 Begin: 2 D ← {} //初始化D 3 while B!= empty do 4 m←argmax S //取出最高的得分 5 M←bm //取出得分最高对应的检测框 6 D←D∪M //更新D 7 B←B─M //更新B 8 for bi in B do 9 idx←IOU(M, B)≥Nt //取出IOU值大于阈值Nt的下标 10 M ← B[idx]/C[idx]/sum(1/C[idx]) //按方差的倒数加权去和得到新的检测框 11 end for 12 endwhile 13 return D, S //返回检测框和对应的分数 14 end 
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表 1 5种算法的性能对比
Table 1. Performance comparison of five algorithms
Method P/% R/% AP/% 速度/(f/s) Faster R-CNN 89.63 70.5 70.65 21.6 YOLOv2 86.45 63.18 71.53 44.2 YOLOv3 92.56 78.5 75.64 33.2 Road_Net 94.18 85.71 79.97 58.7 Road_Net
+M-Softer-NMS95.29 91.12 82.41 57.9
下载: 导出CSV
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