This paper presents a novel tree trunk detection algorithm that uses the Viola and Jones detector along with a proposed pre-processing method, combined with tree trunk detection via depth information. The proposed method tackles the issue of the high false positive rate when the Viola and Jones detector is used on its own, due to the low contrast between tree trunks and the surrounding environment. The pre-processing method uses colour space combination and segmentation to eliminate the ground not covered by trees from the images and feeding the resulting image into a cascade detector for identifying the location of the trunks in the image. Depth information is obtained via the use of the Microsoft KINECT sensor to further increase the accuracy of the detector. Our proposed method had better performance when compared to both Neural Network based and Support Vector Machine based detectors with a detection rate of 91.7% and had the lowest false acceptance rate out of other detectors, including the original Viola and Jones detector. The performance of the proposed method was also tested on live video feeds with the use of a robot prototype in an oil-palm plantation, which proved the high accuracy of the method, with a 97.8% detection rate. The inclusion of depth information resulted in more accurate detections during low levels of light and at night, where reliance on pure depth information resulted in a 100% detection rate of tree trunks within the range of the sensor.
['Mohammed Ayoub Juman', 'Yee Wan Wong', 'Rajprasad Rajkumar', 'Lay Jian Goh']
A novel tree trunk detection method for oil-palm plantation navigation