Detection and segmentation of overlapped fruits based on optimized mask R-CNN application in apple harvesting robot

Jia, Weikuan; Ye, Tian; Luo, Rong; Zhang, Zhonghua; Lian, Jian; Zheng, Yuanjie

doi:10.1016/j.compag.2020.105380

Cited by 240 publications

(111 citation statements)

References 20 publications

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“…Identification of fruit fly adult species based on machine vision [ 43 ] or the method of identifying and counting other insects [ 44 , 45 , 46 ] is mature and has practical applications. At present, using the popular deep learning object detection algorithm, as yolo [ 47 , 48 ] and maskRCNN [ 49 , 50 ] identification can also achieve better results. There are a few methods to detect the grooming behavior of flies.…”

Section: Discussionmentioning

confidence: 99%

Application of Spatio-Temporal Context and Convolution Neural Network (CNN) in Grooming Behavior of Bactrocera minax (Diptera: Trypetidae) Detection and Statistics

Zhang

Zhan

et al. 2020

Insects

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Statistical analysis and research on insect grooming behavior can find more effective methods for pest control. Traditional manual insect grooming behavior statistical methods are time-consuming, labor-intensive, and error-prone. Based on computer vision technology, this paper uses spatio-temporal context to extract video features, uses self-built Convolution Neural Network (CNN) to train the detection model, and proposes a simple and effective Bactrocera minax grooming behavior detection method, which automatically detects the grooming behaviors of the flies and analysis results by a computer program. Applying the method training detection model proposed in this paper, the videos of 22 adult flies with a total of 1320 min of grooming behavior were detected and analyzed, and the total detection accuracy was over 95%, the standard error of the accuracy of the behavior detection of each adult flies was less than 3%, and the difference was less than 15% when compared with the results of manual observation. The experimental results show that the method in this paper greatly reduces the time of manual observation and at the same time ensures the accuracy of insect behavior detection and analysis, which proposes a new informatization analysis method for the behavior statistics of Bactrocera minax and also provides a new idea for related insect behavior identification research.

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Section: Discussionmentioning

confidence: 99%

Application of Spatio-Temporal Context and Convolution Neural Network (CNN) in Grooming Behavior of Bactrocera minax (Diptera: Trypetidae) Detection and Statistics

Zhang

Zhan

et al. 2020

Insects

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“…In the study of fruit detection, apple fruit detection and branch segmentation are the focus of researchers [30][31][32][33]; The establishment of a dedicated neural network for mango detection continues to emerge [34][35][36][37] ； Various neural networks in litchi [38,39], grape [40,41], strawberry [42,43] have achieved good results in their application. The detection of pomelo [44], kiwi fruit [45], waxberry [46], guava [47], and other fruits have been gradually concerned; With the development of deep learning, fruit flower detection, which is difficult to the traditional algorithm, has been emerging [48][49][50][51].…”

Section: B Research On Fruit and Vegetable Detectionmentioning

confidence: 99%

Fast and Accurate Detection of Banana Fruits in Complex Background Orchards

Duan

Zou

et al. 2020

IEEE Access

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The detection of banana fruits is an important part of intelligent management in the banana plantation. To detect the banana fruit quickly and accurately in the complex orchard environment, this paper proposes a method based on the latest deep learning algorithm to detect the banana fruit. Using a monocular camera, we applied the YOLOv4 neural network algorithm to extract the deep features of banana fruits, realizing accurate detection of different banana sizes. The detection algorithm achieved a 99.29% detection rate, the average execution time was 0.171s, the shortest execution time was 0.135s, and the AP was 0.9995. Moreover, the detection results were discussed with the YOLOv3 algorithm and the machine learning algorithm. Compared with the machine learning algorithm, deep learning algorithm was superior to both detection accuracy and detection time. YOLOv4 had higher detection confidence and higher detection rate than YOLOv3. The results show that the proposed method could realize the fast detection of different varieties and different maturity in banana plantations, under different illumination and occlusion conditions, and provide information for banana picking, maturity and yield estimation. INDEX TERMS Banana detection, orchard environment, deep learning, green fruit, YOLOv4. L.Fu et al.: Fast and Accurate Detection of Banana Fruits in Complex Background Orchards L.Fu et al.: Fast and Accurate Detection of Banana Fruits in Complex Background Orchards

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“…Since there are four detecting layers in the network of our approach, we select 12 clusters (anchor boxes) and three anchor boxes for each detection scale. The sizes of the anchor boxes for the RSOD dataset are as follows: (21,24), (25,31), (33,41), (51,54) The sizes of the corresponding anchor boxes for the RSOD dataset and UCS-AOD dataset are shown in Table 5.…”

Section: Anchor Boxes Of Our Modelmentioning

confidence: 99%

“…We exhibit the confusion matrix in Table 6: Table 6. The confusion matrix [51]. As shown in Table 6, TP denotes the sample that is positive in actuality and positive in prediction; FP denotes the sample that is negative in actuality but positive in prediction; FN denotes the sample that is positive in actuality but negative in prediction; TN denotes the sample that is negative in actuality and negative in prediction.…”

Section: The Evaluation Indicatorsmentioning

confidence: 99%

MRFF-YOLO: A Multi-Receptive Fields Fusion Network for Remote Sensing Target Detection

Xu¹,

Wu²

2020

Remote Sensing

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High-altitude remote sensing target detection has problems related to its low precision and low detection rate. In order to enhance the performance of detecting remote sensing targets, a new YOLO (You Only Look Once)-V3-based algorithm was proposed. In our improved YOLO-V3, we introduced the concept of multi-receptive fields to enhance the performance of feature extraction. Therefore, the proposed model was termed Multi-Receptive Fields Fusion YOLO (MRFF-YOLO). In addition, to address the flaws of YOLO-V3 in detecting small targets, we increased the detection layers from three to four. Moreover, in order to avoid gradient fading, the structure of improved DenseNet was chosen in the detection layers. We compared our approach (MRFF-YOLO) with YOLO-V3 and other state-of-the-art target detection algorithms on an Remote Sensing Object Detection (RSOD) dataset and a dataset of Object Detection in Aerial Images (UCS-AOD). With a series of improvements, the mAP (mean average precision) of MRFF-YOLO increased from 77.10% to 88.33% in the RSOD dataset and increased from 75.67% to 90.76% in the UCS-AOD dataset. The leaking detection rates are also greatly reduced, especially for small targets. The experimental results showed that our approach achieved better performance than traditional YOLO-V3 and other state-of-the-art models for remote sensing target detection.

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Detection and segmentation of overlapped fruits based on optimized mask R-CNN application in apple harvesting robot

Cited by 240 publications

References 20 publications

Application of Spatio-Temporal Context and Convolution Neural Network (CNN) in Grooming Behavior of Bactrocera minax (Diptera: Trypetidae) Detection and Statistics

Application of Spatio-Temporal Context and Convolution Neural Network (CNN) in Grooming Behavior of Bactrocera minax (Diptera: Trypetidae) Detection and Statistics

Fast and Accurate Detection of Banana Fruits in Complex Background Orchards

MRFF-YOLO: A Multi-Receptive Fields Fusion Network for Remote Sensing Target Detection

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