关键词: 苹果检测, QT, ARM, OpenCV, 机器视觉

Abstract: In-field presorting for apples can reduce production costs such as storage, packaging, processing etc, and it plays a very important role in improving the economic income of growers and increasing the economic efficiency of enterprise. To overcome the traditional classification detection system disadvantages such as the huge size, difficulty of real-time application in field, apple In-filed presorting and detecting system is designed and implemented based on ARM11 processor which is named S3C6410 and runs pruned Linux software platform. Image acquisition is based on Linux V4L2 framework, CMOS camera acquires apple’s image real-time；ARM is utilized to call OpenCV machine vision processing algorithms which is based on cascade Adaboost target detection algorithm and Haar-like characteristics, detecting apple’s defects and size in time；sending the results to the actuator completing apple's separation. The system control interface is compiled with QT and multi-threading technology, control buttons responding immediately. Preliminary experiments show that the average detection time for an apple is 300 ms and average accuracy is 93%. Compared with the traditional apple detection system, this system has the advantages of high detection speed, low cost, small size, which is suitable for In-field presorting for apples.

Key words: apple detection, QT, ARM, OpenCV, machine vision