Lodging is a major yield-limiting factor in wheat, so we need a method to collect wheat lodging information efficiently Traditional extraction of crop lodging is cost intensive and time consuming; satellite remote sensing technology is vulnerable to the cloud and is limited by its temporal and spatial resolution Therefore, a new method for wheat lodging extraction based on a watershed algorithm combined with an adaptive threshold segmentation hybrid algorithm is proposed An unmanned aerial vehicle equipped with a high-resolution thermal infrared and visible sensor was used to acquire images of lodged and nonlodged wheat Results indicate that there are morphological differences between lodged and nonlodged wheat and that the spectral reflectance and temperature of lodged wheat is higher than that of nonlodged wheat The watershed algorithm is sensitive to the edge response and can reflect the contour characteristics of the lodging area However, the impact of noise is obvious; there are salt-and-pepper patches The wheat lodging ratios were calculated as 1781%, with an accuracy of 8026% With adaptive threshold segmentation, the wheat lodging ratios were calculated as 1878%, with an accuracy of 8465% If these two methods are fused, the influence of noise can be reduced, and an extraction accuracy of 9358% can be achieved This research provides a new idea for highly efficient crop lodging data collection, which can be used for precision farming and the selection of crop genotypes with high lodging resistance

Use of unmanned aerial vehicle imagery and a hybrid algorithm combining a watershed algorithm and adaptive threshold segmentation to extract wheat lodging

Application and improvement of Canny edge-detection algorithm for exterior wall hollowing detection using infrared thermal images

This paper describes an improved Canny edge detection algorithm to detect edge of infrared image better through overcoming the complexity and intensity of infrared image noise acquired by missile-borne radar seeker. Based on the traditional Canny algorithm architecture, the improved Sequential Statistical Filtering (ISSF) algorithm is used to replace Gauss filtering algorithm, and the P-entropy method (PEM) and OTSU threshold (PEM and OTSU, POM) are used to self-adaptively select the two thresholds for edge extraction. Experiments show that the algorithm can eliminate noise interference better and has higher edge detection effect for infrared images.

Canny Edge Detection Algorithm Based on Improved Sequential Statistical Filter

Human-vehicle cooperative driving has become one of the critical stages to achieve a higher level of driving automation. For an autonomous driving system, the complex traffic environments bring great challenges to its visual perception tasks. Based on the gaze points of human drivers and the images detected from a semi-automated vehicle, this work proposes a framework to fuse their visual characteristics based on the Laplacian Pyramid algorithm. By adopting Extended Kalman Filter, we improve the detection accuracy of objects with interaction risk. This work also reveals that the cooperative visual perception framework can predict the trajectory of objects with interaction risk better than simple object detection algorithms. The findings can be applied in improving visual perception ability and making proper decisions and control for autonomous vehicles. 

Human-Vehicle Cooperative Visual Perception for Autonomous Driving Under Complex Traffic Environments

The traditional Canny operator cannot get the optimal threshold in different scene, on this foundation, an improved Canny edge detection algorithm based on adaptive threshold is proposed. The result of the experiment pictures indicate that the improved algorithm can get responsible threshold, and has the better accuracy and precision in the edge detection.

Huaisuo Wang

Papers

Research of adaptive threshold edge detection algorithm based on statistics canny operator