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Smart Agriculture ›› 2020, Vol. 2 ›› Issue (1): 99-110.doi: 10.12133/j.smartag.2020.2.1.202001-SA004

• 专题--农业遥感与表型信息获取分析 • 上一篇    下一篇

基于轻量级无锚点深度卷积神经网络的树上苹果检测模型

夏雪1,2, 孙琦鑫1,2, 侍啸1,2, 柴秀娟1,2()   

  1. 1.中国农业科学院农业信息研究所,北京 100081
    2.农业农村部农业大数据重点实验室,北京 100081
  • 收稿日期:2020-01-21 修回日期:2020-02-19 出版日期:2020-03-30 发布日期:2020-04-17
  • 基金资助:
    国家自然科学基金面上项目(61976219);中国农业科学院农业信息研究所基本科研业务费项目(JBYW-AII-2019-18);中国农业科学院科技创新工程项目(CAAS-ASTIP-2016-AII)
  • 作者简介:夏 雪(1983-),男,博士,助理研究员,研究方向:果树表型研究与应用,Email:xiaxue@caas.cn
  • 通讯作者: 柴秀娟(1978-),女,博士,研究员,研究方向:机器视觉、智能感知、农业机器人,电话:13911671540, E-mail:chaixiujuan@caas.cn

Apple detection model based on lightweight anchor-free deep convolutional neural network

Xia Xue1,2, Sun Qixin1,2, Shi Xiao1,2, Chai Xiujuan1,2()   

  1. 1.Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    2.Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
  • Received:2020-01-21 Revised:2020-02-19 Online:2020-03-30 Published:2020-04-17
  • corresponding author: Xiujuan Chai E-mail:chaixiujuan@caas.cn

摘要:

为提高现有苹果目标检测模型在硬件资源受限制条件下的性能和适应性,实现在保持较高检测精度的同时,减轻模型计算量,降低检测耗时,减少模型计算和存储资源占用的目的,本研究通过改进轻量级的MobileNetV3网络,结合关键点预测的目标检测网络(CenterNet),构建了用于苹果检测的轻量级无锚点深度学习网络模型(M-CenterNet),并通过与CenterNet和单次多重检测器(Single Shot Multibox Detector,SSD)网络比较了模型的检测精度、模型容量和运行速度等方面的综合性能。对模型的测试结果表明,本研究模型的平均精度、误检率和漏检率分别为88.9%、10.9%和5.8%;模型体积和帧率分别为14.2MB和8.1fps;在不同光照方向、不同远近距离、不同受遮挡程度和不同果实数量等条件下有较好的果实检测效果和适应能力。在检测精度相当的情况下,所提网络模型体积仅为CenterNet网络的1/4;相比于SSD网络,所提网络模型的AP提升了3.9%,模型体积降低了84.3%;本网络模型在CPU环境中的运行速度比CenterNet和SSD网络提高了近1倍。研究结果可为非结构环境下果园作业平台的轻量化果实目标检测模型研究提供新的思路。

关键词: 机器视觉, 深度学习, 轻量级网络, 无锚点, 苹果检测

Abstract:

Intelligent production and robotic oporation are the efficient and sustainable agronomic route to cut down economic and environmental costs and boosting orchard productivity. In the actual scene of the orchard, high performance visual perception system is the premise and key for accurate and reliable operation of the automatic cultivation platform. Most of the existing apple detection models, however, are difficult to be used on the platforms with limited hardware resources in terms of computing power and storage capacity due to too many parameters and large model volume. In order to improve the performance and adaptability of the existing apple detection model under the condition of limited hardware resources, while maintaining detection accuracy, reducing the calculation of the model and the model computing and storage footprint, shorten detection time, this method improved the lightweight MobileNetV3 and combined the object detection network which was based on keypoint prediction (CenterNet) to build a lightweight anchor-free model (M-CenterNet) for apple detection. The proposed model used heatmap to search the center point (keypotint) of the object, and predict whether each pixel was the center point of the apple, and the local offset of the keypoint and object size of the apple were estimated based on the extracted center point without the need for grouping or Non-Maximum Suppression (NMS). In view of its advantages in model volume and speed, improved MobileNetV3 which was equipped with transposed convolutional layers for the better semantic information and location information was used as the backbone of the network. Compared with CenterNet and SSD (Single Shot Multibox Detector), the comprehensive performance, detection accuracy, model capacity and running speed of the model were compared. The results showed that the average precision, error rate and miss rate of the proposed model were 88.9%, 10.9% and 5.8%, respectively, and its model volume and frame rate were 14.2MB and 8.1fps. The proposed model is of strong environmental adaptability and has a good detection effect under the circumstance of various light, different occlusion, different fruits’ distance and number. By comparing the performance of the accuracy with the CenterNet and the SSD models, the results showed that the proposed model was only 1/4 of the size of CenterNet model while has comparable detection accuracy. Compared with the SSD model, the average precision of the proposed model increased by 3.9%, and the model volume decreased by 84.3%. The proposed model runs almost twice as fast using CPU than the CenterNet and SSD models. This study provided a new approach for the research of lightweight model in fruit detection with orchard mobile platform under unstructured environment.

Key words: machine vision, deep learning, lightweight network, anchor-free, apple detection

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