基于OpenCV實現(xiàn)蘋果圖像識別

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摘 要
計算機視覺檢測技術(shù)是檢測技術(shù)中一個新興的應(yīng)用方向和備受關(guān)注的前沿課題，是計算機技術(shù)、模式識別、檢測技術(shù)、數(shù)字圖像處理、人工智能等多門學(xué)科的結(jié)晶。如今，計算機視覺技術(shù)正在向更智能化的方向發(fā)展，即不需要人為干預(yù)，便可利用圖像處理、模式識別等方法，獲取一定區(qū)域內(nèi)的信息并自動分析，實現(xiàn)對場景目標的識別、定位甚至跟蹤，得出對圖像內(nèi)容含義以及客觀場景的理解，最終給出檢測結(jié)果。在農(nóng)業(yè)機械方面，農(nóng)業(yè)機器人已經(jīng)逐步應(yīng)用到農(nóng)業(yè)生產(chǎn)中，特別是在設(shè)施農(nóng)業(yè)的生產(chǎn)過程中，機器人的使用將會成為現(xiàn)代農(nóng)業(yè)向自動化和智能化發(fā)展的重要標志。但是，相對于國外，我國對農(nóng)業(yè)機器人這一領(lǐng)域的研究還相對落后，且離實際應(yīng)用還有很大距離。所以，要想轉(zhuǎn)變農(nóng)業(yè)格局、優(yōu)化農(nóng)業(yè)結(jié)構(gòu)、發(fā)展新式農(nóng)業(yè)，必須對農(nóng)業(yè)機器人進行更深入的研究。
本文主要研究農(nóng)業(yè)采摘機器人視覺系統(tǒng)中的圖像處理部分，以成熟的蘋果圖像為研究對象，考慮蘋果采摘的實際操作要求，利用計算機圖像處理技術(shù)及特征提取技術(shù)，從蘋果的顏色、形狀等特征著手，在Linux系統(tǒng)上運用OpenCV等軟件，對圖像進行預(yù)處理，然后使用閾值分割法、分水嶺算法等方法對自然情況下成熟蘋果圖像進行分割、識別，為下一步實現(xiàn)蘋果的準確定位與無損采摘打下基礎(chǔ)。
此次研究的主要內(nèi)容有：
1.實驗的總體規(guī)劃和整體設(shè)計。本次實驗著重于采摘機器人系統(tǒng)中的圖像處理環(huán)節(jié)，運用以C/C++語言為基礎(chǔ)的開放源代碼的OpenCV軟件進行圖像處理。OpenCV具有強大的圖像和矩陣運算能力，提供了高效和豐富的圖像處理算法，使用起來較為簡單、快捷。研究中先對蘋果圖像進行預(yù)處理，再運用分水嶺算法對蘋果圖像進行分割、識別。
2.圖像的預(yù)處理。果實在成長過程中受各種因素的影響，導(dǎo)致表面受到不均勻的光照，使得采集到的蘋果圖像偏暗或偏亮。針對自然光照條件下采集果實圖像的特點，采用直方圖均衡化和高斯去噪的方法，增強圖像的亮度及對比度，這樣既保持果實圖像的邊緣和細節(jié)，又并不增加新顏色。下一步選用HSV顏色空間模型中的H分量，為圖像分割做準備。
3.基于分水嶺算法圖像的分割。選用蘋果的形狀和顏色作為特征提取對象，通過對預(yù)處理后的蘋果圖像進行閾值分割，將圖像粗略的分割成兩大區(qū)域——蘋果、背景；然后運用數(shù)學(xué)形態(tài)學(xué)中的膨脹、腐蝕等運算操作，去除噪點，填充蘋果的小空洞，并平滑各區(qū)域的邊界；接下來用距離法標識每個點離邊緣的距離，然后用閾值法把兩個連在一起的蘋果分離開，再將標定里確定的背景、各個蘋果及不確定的部分分別用不同編號標識；最后調(diào)用OpenCV分水嶺算法，灌注各個部分，并確認出最終的邊界。這樣便將圖像中兩個相連的蘋果從背景中識別出來并相互區(qū)分開來。

關(guān)鍵詞：農(nóng)業(yè)機器人；蘋果采摘；圖像處理；OpenCV；圖像分割；分水嶺算法

ABSTRACT
Computer vision detection technology is an emerging technology to detect the direction and application topics at the forefront of concern, is the crystallization of computer technology, pattern recognition, detection technology, digital image processing, artificial intelligence, and many other disciplines. Today, computer vision technology is to a more intelligent direction that does not require human intervention, you can take advantage of image processing, pattern recognition and other methods to obtain information in a certain area and automatically analyze the scene to identify the target to achieve, even positioning follow, come on image content and objective of the scene to understand the meaning of the final test results are given. Agricultural machinery, agricultural robots have gradually applied to agricultural production, especially in the agricultural production process facilities, the use of robots will become an important symbol of modern agriculture to automation and intelligent development. However, compared to other countries, our research in this area for agricultural robot is relatively backward, and there is a great distance away from the actual application. Therefore, in order to transform the agricultural structure, optimizing agricultural structure, develop new agriculture, agricultural robot must be more in-depth study.
This paper studies the agricultural picking robot vision system image processing section to mature apple image as the research object, consider the practical requirements of apple picking, using computer image processing technology and feature extraction technology from Apple's color, shape and other characteristics to proceed, using OpenCV on Linux systems, such as software, image preprocessing, and then use the threshold segmentation method, and other methods of watershed algorithm ripe apple image segmentation under natural conditions, identification, as the next step to achieve accurate positioning and Apple lossless picking basis.
The main contents are:
1. Overall planning and overall design of the experiment. The experiment focused on picking robot system image processing chain, the use in C / C + + language -based open source OpenCV software for image processing, OpenCV has a powerful image and matrix operations capabilities, provides an efficient and rich image processing algorithms, relatively simple to use and fast. Apple first study image preprocessing, then use Apple watershed image segmentation algorithm to identify.
2. The image preprocessing. In the process of growing fruit affected by variou..