The accurate identification of overlapping Agaricus bisporus in a factory environment is one of the challenges faced by automated picking. In order to better segment the complex adhesion between Agaricus bisporus, this paper proposes a segmentation recognition algorithm for overlapping Agaricus bisporus. This algorithm calculates the global gradient threshold and divides the image according to the image edge gradient feature to obtain the binary image. Then, the binary image is filtered and morphologically processed, and the contour of the overlapping Agaricus bisporus area is obtained by edge detection in the Canny operator, the convex hull and concave area are extracted for polygon simplification, and the vertices are extracted using Harris corner detection to determine the segmentation point. After dividing the contour fragments by the dividing point, the branch definition algorithm is used to merge and group all the contours of the same Agaricus bisporus. Finally, the least squares ellipse fitting algorithm and the minimum distance circle fitting algorithm are used to reconstruct the outline of Agaricus bisporus, and the demand information of Agaricus bisporus picking is obtained. The experimental results show that this method can effectively overcome the influence of uneven illumination during image acquisition and be more adaptive to complex planting environments. The recognition rate of Agaricus bisporus in overlapping situations is more than 96%, and the average coordinate deviation rate of the algorithm is less than 1.59%.
AbstractDifferent leakage detection technologies present different efficiency and precision of leakage detection in water distribution systems due to their own properties. This study aimed to investigate the use of acoustic sensors to locate the exact position of leaks, which work 24 hours per day in different modes, i.e. fixed installation and mobile exploration. The results showed that the maximum distance between the acoustic sensors installed on the pipeline was 380 m. Field application results indicated that detecting threshold of the distance was 300 m and the intensity ratio of leaking state noise (LSN) to background noise was 1.9 respectively. The pipeline length by manual hunting area (MHA) is 1.3 times longer than the acoustic logger tested area (ALTA), but the detected leakage points in MHA is 20% less than in ALTA. Furthermore, the cost of leakage detection in MHA was about 6,000 yuan/detected leakage point. That in ALTA was about 2,500 yuan/detected leakage point. The case study also indicates that the acoustic logger is a promising tool with the advantage of lower cost and faster detection, compared to manual ground listening with a sound stick. The acoustic leakage detection method can detect the leakage in different sized pipelines, whereas the manual detection was concentrated in small-sized pipelines.
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