弱光照條件下交通標志檢測與識別

趙坤; 劉立; 孟宇; 孫若燦

doi:10.13374/j.issn2095-9389.2019.08.14.003

弱光照條件下交通標志檢測與識別

doi: 10.13374/j.issn2095-9389.2019.08.14.003

北京科技大學機械工程學院，北京 100083

基金項目: 國家重點研發計劃資助項目（2018YFE0192900，2018YFC0810500，2018YFC0604403）；國家高技術研究發展計劃資助項目（2011AA060408）；中央高校基本科研業務費專項資金資助項目（FRF-TP-17-010A2）

詳細信息

通訊作者:
E-mail：myu@ustb.edu.cn

中圖分類號: TP391.4
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- 文章訪問數: 1992
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- 被引次數: 0
出版歷程
- 收稿日期: 2019-08-14
- 刊出日期: 2020-09-11

Traffic signs detection and recognition under low-illumination conditions

School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China

More Information

Corresponding author: E-mail: myu@ustb.edu.cn

摘要

摘要: 針對弱光照條件下交通標志易發生漏檢和定位不準的問題，本文提出了增強YOLOv3（You only look once）檢測算法，一種實時自適應圖像增強與優化YOLOv3網絡結合的交通標志檢測與識別方法。首先構建了大型復雜光照中國交通標志數據集；然后針對復雜的弱光照圖像提出自適應增強算法，通過調整圖像亮度和對比度強化交通標志與背景之間的差異；最后采用YOLOv3網絡框架檢測交通標志。為了降低先驗錨點框設置精度以及圖像中背景與前景比例嚴重失衡對檢測精度造成的影響，優化了先驗錨點框聚類算法和網絡的損失函數。對比實驗結果表明，在實時性大致相當的情況下，本文提出的增強YOLOv3檢測算法較標準YOLOv3算法對交通標志有更高的回歸精度和置信度，召回率和準確率分別提高0.96%和0.48%。
- 交通標志檢測 /
- 弱光照 /
- 自適應圖像增強 /
- YOLOv3 /
- 深度學習
Abstract: Traffic sign detection and recognition, which are important to ensure traffic safety, have been a research hotspot. In recent years, with the rapid development of automated driving technology, significant progress has been made in developing more accurate and efficient deep learning algorithms for traffic sign detection and recognition. However, these studies mainly focus on foreign traffic signs and do not consider the low-illumination conditions in practical application, which is a common scene. Therefore, many challenges still exist in the application of traffic sign detection and recognition in traffic scenes. To solve the problems of easy omission and inaccurate positioning for traffic sign detection and recognition under complex illumination conditions, the enhanced YOLOv3 (You only look once) detection algorithm, a traffic sign detection and recognition method combining real-time adaptive image enhancement and the YOLOv3 frame was proposed. First, a large and complex illumination traffic sign dataset for Chinese traffic was constructed; it included globally low illumination, locally low illumination, and sufficient illumination images. Then an adaptive enhancement algorithm was proposed for low-illumination images, which can enhance the difference between traffic signs and background by adjusting the brightness and contrast of the images. Finally, high-quality and discrimination images as input were transmitted to the YOLOv3 network framework, and traffic sign detection and recognition were performed. To reduce the influences of the prior anchor box setting accuracy and the imbalance between the background and foreground on the detection accuracy, the clustering algorithm for the prior anchor box and loss function for the network were optimized. The results of the comparison experiment with the LISA dataset and complex illumination traffic sign dataset for Chinese traffic show that the proposed enhanced YOLOv3 detection algorithm has higher regression accuracy and category confidence than the published YOLOv3 algorithm for traffic signs; the recall and precision are higher by 0.96% and 0.48%, respectively, which indicates the application potential of the proposed algorithm in actual traffic scenarios.
- traffic sign detection /
- low illumination /
- adaptive image enhancement /
- YOLOv3 /
- deep learning

HTML全文

圖 1 不同天氣及光照條件的圖像樣本. （a）陰天; （b）雨雪天; （c）光照充足; （d）光照不足

Figure 1. Image samples under different weather and illumination conditions: (a) overcast; (b) rain and snow; (c) sufficient illumination; (d) insufficient illumination

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圖 2 交通標志數據分布示意圖

Figure 2. Data distribution diagram for traffic signs

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圖 3 自適應Gamma校正流程圖

Figure 3. Flow diagram of adaptive gamma correction

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圖 4 聚類中心數目測試結果

Figure 4. Test results of number for cluster centers

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圖 5 優化前后損失值示意圖

Figure 5. Loss value before and after optimization

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圖 6 網絡參數圖

Figure 6. Network parameter diagram

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圖 7 整體光照不足的圖像. （a）圖像處理前；（b）圖像處理后；（c）圖像處理前的像素概率直方圖；（d）圖像處理后的像素概率直方圖

Figure 7. Images with low overall illumination: (a) image before processing; (b) image after processing; (c) pixel probability histograms of image before processing; (d) pixel probability histograms of image after processing

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圖 9 光照充足圖像。（a）圖像處理前；（b）圖像處理后；（c）圖像處理前像素概率直方圖；（d）圖像處理后像素概率直方圖

Figure 9. Images with sufficient illumination: (a) image before processing; (b) image after processing; (c) pixel probability histograms of image before processing; (d) pixel probability histograms of image after processing

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圖 8 局部光照不足圖像。（a）圖像處理前；（b）圖像處理后；（c）圖像處理前像素概率直方圖；（d）圖像處理后像素概率直方圖

Figure 8. Images with low local illumination: (a) image before processing; (b) image after processing; (c) pixel probability histograms of image before processing; (d) pixel probability histograms of image after processing

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圖 10 不同算法測試結果可視化對比. （a, b）標準YOLOv3；（c, d）改進YOLOv3

Figure 10. Visual comparison of different algorithm for test results: (a, b) standard YOLOv3; (c, d) improved YOLOv3

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圖 11 不同算法測試結果可視化對比。（a, b）標準YOLOv3；（c, d）增強YOLOv3

Figure 11. Visual comparison of different algorithms for test results: (a, b) standard YOLOv3; (c, d) enhanced YOLOv3

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表 1 圖像分類

Table 1. Image classification

Contrast category	Intensity mean, λ	Image category
I_L	≥ 0.5	Low contrast and high brightness
I_L	< 0.5	Low contrast and low brightness
I_H	≥ 0.5	High contrast and high brightness
I_H	< 0.5	High contrast and low brightness

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表 2 在LISA數據集上的測試結果（閾值=0.8，IOU=0.7）

Table 2. Test results on LISA dataset (threshold = 0.8, IOU = 0.7)

Algorithm	Number of traffic signs	Recall/%	Accuracy/%
Standard YOLOv3	1446	88.80	99.07
Improved YOLOv3	1446	91.36	98.44

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表 3 在弱光照交通標志數據集上的測試結果（閾值=0.8，IOU=0.7）

Table 3. Test results on weak illumination traffic signs dataset (threshold = 0.8, IOU = 0.7)

Algorithm	Dark images with 2163 traffic signs		Bright images with 2315 traffic signs		All images with 4478 traffic signs		Run time/ms
Algorithm	Recall/%	Accuracy/%	Recall/%	Accuracy/%	Recall/%	Accuracy/%	Run time/ms
Standard YOLOv3	96.30	98.91	98.49	99.30	97.43	99.11	33
Enhanced YOLOv3	98.06	99.44	98.70	99.74	98.39	99.59	36

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