Study on Image Detection Method of Navigation Route for Cotton Harvester

2012 ◽  
Vol 246-247 ◽  
pp. 219-224 ◽  
Author(s):  
Jing Bin Li ◽  
Bing Qi Chen ◽  
Yang Liu ◽  
Tao Zha
Keyword(s):  
Hough Transform ◽  
Processing Time ◽  
Detection Method ◽  
Point Group ◽  
Color Difference ◽  
Detection Algorithm ◽  
Boundary Line ◽  
Working Process ◽  
Image Detection ◽  
Candidate Point

This paper presents an image detection algorithm for navigation route of cotton harvester. Two cameras were respectively installed on the leftmost and rightmost picker unit, and images were captured during working process respectively. Firstly, the color characteristics among harvested field, un-harvested field, outside-field and the end of field were analyzed, then the target features of different fields was extracted using the color difference 3B-R-G. Secondly, candidate point group was determined by looking for the critical point of peak from the lowest trough point to un-harvested field and associating with the detection result of the anterior frame. Lastly, navigation line was obtained by using passing a Known Point Hough Transform (PKPHT). Results show that the navigation line detected using this algorithm can fit the boundary line and the edge of field accurately, the average processing time is56.10ms/f, and the algorithm can meet the actual production needs of cotton harvester.

scholarly journals Detection method for auto guide vehicle’s walking deviation based on image thinning and Hough transform

2019 ◽  
Vol 52 (3-4) ◽  
pp. 252-261 ◽  
Author(s):  
Xiaohua Cao ◽  
Daofan Liu ◽  
Xiaoyu Ren
Keyword(s):  
Hough Transform ◽  
Detection Method ◽  
Detection Algorithm ◽  
Visual Navigation ◽  
Guide Line ◽  
Actual Environment ◽  
Lane Line ◽  
Image Thinning ◽  
High Level ◽  
Refinement Algorithm

Auto guide vehicle’s position deviation always appears in its walking process. Current edge approaches applied in the visual navigation field are difficult to meet the high-level requirements of complex environment in factories since they are easy to be affected by noise, which results in low measurement accuracy and unsteadiness. In order to avoid the defects of edge detection algorithm, an improved detection method based on image thinning and Hough transform is proposed to solve the problem of auto guide vehicle’s walking deviation. First, the image of lane line is preprocessed with gray processing, threshold segmentation, and mathematical morphology, and then, the refinement algorithm is employed to obtain the skeleton of the lane line, combined with Hough detection and line fitting, the equation of the guide line is generated, and finally, the value of auto guide vehicle’s walking deviation can be calculated. The experimental results show that the methodology we proposed can deal with non-ideal factors of the actual environment such as bright area, path breaks, and clutters on road, and extract the parameters of the guide line effectively, after which the value of auto guide vehicle’s walking deviation is obtained. This method is proved to be feasible for auto guide vehicle in indoor environment for visual navigation.

Research on the Lane Detection Algorithm Based on Zoning Hough Transformation

2012 ◽  
Vol 490-495 ◽  
pp. 1862-1866 ◽  
Author(s):  
Chao Fan ◽  
Li Long Hou ◽  
Shuai Di ◽  
Jing Bo Xu
Keyword(s):  
Hough Transform ◽  
Ambient Noise ◽  
Detection Method ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Dominant Position ◽  
Hough Transformation ◽  
The Road ◽  
Lane Marking ◽  
Sobel Operators

In order to improve the adaptability of the lane detection algorithm under complex conditions such as damaged lane lines, covered shadow, insufficient light, the rainy day etc. Lane detection algorithm based on Zoning Hough Transform is proposed in this paper. The road images are processed by the improved ±45° Sobel operators and the two-dimension Otsu algorithm. To eliminate the interference of ambient noise, highlight the dominant position of the lane, the Zoning Hough Transform is used, which can obtain the parameters and identify the lane accurately. The experiment results show the lane detection method can extract the lane marking parameters accurately even for which are badly broken, and covered by shadow or rainwater completely, and the algorithm has good robustness.

Research on the Condom Image Detection Algorithm for Automatic Sorting

2011 ◽  
Vol 305 ◽  
pp. 315-319
Author(s):  
Zhu Ying ◽  
Chang Hui Li ◽  
Wen Peng Gao
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Hough Transform ◽  
Detection Algorithm ◽  
Processing Method ◽  
Transform Method ◽  
Canny Operator ◽  
Image Detection ◽  
Image Processing Method ◽  
Automatic Sorting

To realize the automatic sorting before electrical testing of condoms, the position of condom ring needs to be discriminated with image processing method. Procedures for ellipse detection are written based on OpenCV, Gauss filter is applied to smooth the collected images, and then Canny operator is used for edge detection, combined with the modified Hough transform method to determine the ellipse center to achieve the effective detection of the condom ring.

scholarly journals Practical limitations of lane detection algorithm based on Hough transform in challenging scenarios

2021 ◽  
Vol 18 (2) ◽  
pp. 172988142110087
Author(s):  
Qiao Huang ◽  
Jinlong Liu
Keyword(s):  
Hough Transform ◽  
Detection Rate ◽  
Real Life ◽  
Weather Conditions ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Search Range ◽  
Range Restriction ◽  
Lighting Conditions

The vision-based road lane detection technique plays a key role in driver assistance system. While existing lane recognition algorithms demonstrated over 90% detection rate, the validation test was usually conducted on limited scenarios. Significant gaps still exist when applied in real-life autonomous driving. The goal of this article was to identify these gaps and to suggest research directions that can bridge them. The straight lane detection algorithm based on linear Hough transform (HT) was used in this study as an example to evaluate the possible perception issues under challenging scenarios, including various road types, different weather conditions and shades, changed lighting conditions, and so on. The study found that the HT-based algorithm presented an acceptable detection rate in simple backgrounds, such as driving on a highway or conditions showing distinguishable contrast between lane boundaries and their surroundings. However, it failed to recognize road dividing lines under varied lighting conditions. The failure was attributed to the binarization process failing to extract lane features before detections. In addition, the existing HT-based algorithm would be interfered by lane-like interferences, such as guardrails, railways, bikeways, utility poles, pedestrian sidewalks, buildings and so on. Overall, all these findings support the need for further improvements of current road lane detection algorithms to be robust against interference and illumination variations. Moreover, the widely used algorithm has the potential to raise the lane boundary detection rate if an appropriate search range restriction and illumination classification process is added.

The Detection Method of Lane Line Based on the Improved Otsu Threshold Segmentation

2015 ◽  
Vol 741 ◽  
pp. 354-358 ◽  
Author(s):  
Yang Shan Tang ◽  
Dao Hua Xia ◽  
Gui Yang Zhang ◽  
Li Na Ge ◽  
Xin Yang Yan
Keyword(s):  
Image Segmentation ◽  
Processing Time ◽  
Detection Method ◽  
Segmentation Algorithm ◽  
Test Results ◽  
Threshold Segmentation ◽  
Otsu Method ◽  
Time Requirements ◽  
Lane Line ◽  
Center Distance

For overcoming the shortage of Otsu method, proposed an improved Otsu threshold segmentation algorithm. On the basis of Otsu threshold segmentation algorithm, the gray level was divided into two classes according to the image segmentation, to determine the best threshold by comparing their center distance, so as to achieve peak line recognition under the condition of multiple gray levels. Then did experiments on image segmentation of the lane line with MATLAB by traditional Otsu threshold segmentation algorithm and the improved algorithm, the threshold of traditional Otsu threshold segmentation algorithm is 144 and the threshold of the improved Otsu threshold segmentation algorithm is 131, the processing time is within 0.453 s. Test results show that the white part markings appear more, the intersection place of white lines and the background is more clear, so this method can identify lane markings well and meet the real-time requirements.

An Efficient Approach for Uncertain Event Detection in RFID Complex Event Processing

2021 ◽  
Vol 5 (4) ◽  
pp. 438
Author(s):  
Siti Salwani Binti Yaacob ◽  
Hairulnizam Bin Mahdin ◽  
Mohammed Saeed Jawad ◽  
Nayef Abdulwahab Mohammed Alduais ◽  
Akhilesh Kumar Sharma ◽  
...  
Keyword(s):  
Processing Time ◽  
Detection Algorithm ◽  
Manufacturing Processes ◽  
Memory Consumption ◽  
Production Flow ◽  
Manufacturing Environment ◽  
Thread Pool ◽  
Missing Items ◽  
Defensive Measures ◽  
Current Detection

The globalization of manufacturing has increased the risk of counterfeiting as the demand grows, the production flow increases, and the availability expands. The intensifying counterfeit issues causing a worriment to companies and putting lives at risk. Companies have ploughed a large amount of money into defensive measures, but their efforts have not slowed counterfeiters. In such complex manufacturing processes, decision-making and real-time reactions to uncertain situations throughout the production process are one way to exploit the challenges. Detecting uncertain conditions such as counterfeit and missing items in the manufacturing environment requires a specialized set of technologies to deal with a flow of continuously created data. In this paper, we propose an uncertain detection algorithm (UDA), an approach to detect uncertain events such as counterfeit and missing items in the RFID distributed system for a manufacturing environment. The proposed method is based on the hashing and thread pool technique to solve high memory consumption, long processing time and low event throughput in the current detection approaches. The experimental results show that the execution time of the proposed method is averagely reduced 22% in different tests, and our proposed method has better performance in processing time based on RFID event streams.

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