基于YOLO v4+HSV的成熟期番茄识别方法

Tomato recognition method at the ripening stage based on YOLO v4 and HSV

为解决成熟番茄采摘识别中由于藤蔓、叶片、果实遮挡或光照影响而引起的误识别问题,该研究提出了一种基于YOLO v4与HSV(Hue,Saturation,Value)相结合的识别方法,以实现自然环境下成熟期番茄的准确识别。在YOLO v4网络的检测框内通过HSV方法对番茄的红色区域进行分割,并将分割面积在检测框中达到一定占比的番茄作为目标输出。通过对比不同占比下该算法对测试集的识别效果,将16%作为成熟期番茄识别算法的占比,该占比下YOLO v4+HSV算法的正确率为94.77%,在工作站中检测单幅图片的速度为25.86 ms。为验证算法的性能,对改进前后算法进行了比较,改进后的正确率比改进前提高了4.30个百分点,说明通过HSV处理能够提高原网络识别成熟期番茄的准确性。此外,为测试算法的实用性,统计了在不同类型设备上该算法从调用深度相机到检测到第一个目标番茄所用的时间,经计算,其在工作站上所用的平均时间为0.51 s,在微型工控机上为1.48 s,均可满足实际采摘需要。该研究可为果蔬采摘的准确高效识别提供借鉴。

An accurate recognition of fruit and vegetable depends mainly on the occlusion of vine,leaf,and light during robotic harvesting at present.In this study,a feasible recognition algorithm was proposed to efficiently identify the ripe tomatoes in the natural environment using YOLO v4 and HSV.The data set of mature tomatoes was also collected to capture some obscure images with the vines and leaves or color-changing by light under the complex growth environment.Once the original YOLO v4 network was utilized to identify the tomatoes after learning these samples,some tomatoes in the green ripening and color transition stage were taken like in the mature stage.Therefore,an HSV processing was added into the detection box of the original YOLO v4 network,in order to segment the red region of tomatoes.The specific tomatoes were taken as the target output to improve the accuracy of recognition if the red areas of segmentation reached a critical proportion in the detection box.The size of the proportion presented an important impact on the accuracy of recognition.The recognition performance was also compared on the test set under different proportions.As such,the proportion of 16%was taken as the tomato recognition at the mature stage.At this time,the highest recognition accuracy of the combined YOLO v4 and HSV was 94.77%,4.30 percentage point higher than that of the original.The detection speed of a single image in the workstation was 25.86 ms.It indicated that the addition of HSV processing was widely expected to improve the accuracy of the original network.Furthermore,the improved network was also used to effectively remove immature tomatoes that cannot be recognized by the improved Cascade RCNN.In addition,the running time was tested ranging from the calling RealSense D435i to the first target tomato on the workstation and the miniature industrial computer.It was found that the average time of recognition was 0.51 s on the workstation,and 1.48 s on the miniature industrial computer,using the combined YOLO v4 and HSV from turning on the camera to the first target detection.Consequently,the improved algorithm was fully met the real-time requirements of mechanical picking.This finding can also provide a strong theoretical basis for the accurate,efficient,and real-time recognition of fruit and vegetable picking using robots in a complex environment.