Deep Orange: Mask R-CNN based Orange Detection and Segmentation

Ganesh, Prashant; Volle, Kyle; Burks, Thomas F.; Mehta, Siddhartha S.

doi:10.1016/j.ifacol.2019.12.499

Cited by 112 publications

(57 citation statements)

References 34 publications

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“…The impact of target dataset diversity and choosing a backbone model that is suitable for the target classes is much more important than the number of sample images in the dataset itself (as shown in Table 1 [ 111 , 113 , 114 ]).…”

Section: Discussionmentioning

confidence: 99%

“…Most of the recently presented models, including YOLO-v3 and R-CNN family architectures, are supported by the FPN model, which enables small object detection and enhances semantic segmentation and multi-object detection (as shown in Table 1 [ 107 , 108 , 110 , 111 , 113 , 114 , 117 , 118 ]).…”

Section: Discussionmentioning

confidence: 99%

“…Texture and colour analysis techniques and high-resolution image samples are recommended to enhance the feature extraction phase (as shown in Table 1 [ 32 , 112 , 113 , 119 ]).…”

Section: Discussionmentioning

confidence: 99%

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Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Hasan

Yusuf

Alzubaidi

2020

Plants

142

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Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Hasan

Yusuf

Alzubaidi

2020

Plants

142

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“…Mask R-CNN improves the loss function of segmentation, from the polynomial cross-entropy based on single-pixel softmax to the binary cross-entropy based on single-pixel sigmoid. 28 If the candidate frame is detected as a category, the binary cross-entropy will make the cross-entropy of this category to be calculated as the error value, only contribute to the ground truth (GT) of the specific K class, while the other classes do not contribute to the loss, 29 and in the back propagation, the loss function L only calculates and back propagates the GT classes, which decouples the segmentation and classification and effectively avoids the competition between classes. Another improvement is that Mask R-CNN adds the RoI align layer, Mask R-CNN uses bilinear interpolation to make the pooling result of the region of interest closer to the features before the non-pooling, thus reducing the error.…”

Section: Related Workmentioning

confidence: 99%

Research on abnormal object detection in specific region based on Mask R-CNN

Xiong

Liu

et al. 2020

International Journal of Advanced Robotic Systems

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As an information carrier with rich semantics, image plays an increasingly important role in real-time monitoring of logistics management. Abnormal objects are typically closely related to the specific region. Detecting abnormal objects in the specific region is conducive to improving the accuracy of detection and analysis, thereby improving the level of logistics management. Motivated by these observations, we design the method called abnormal object detection in a specific region based on Mask R-convolutional neural network: Abnormal Object Detection in Specific Region. In this method, the initial instance segmentation model is obtained by the traditional Mask R-convolutional neural network method, then the region overlap of the specific region is calculated and the overlapping ratio of each instance is determined, and these two parts of information are fused to predict the exceptional object. Finally, the abnormal object is restored and detected in the original image. Experimental results demonstrate that our proposed Abnormal Object Detection in Specific Region can effectively identify abnormal objects in a specific region and significantly outperforms the state-of-the-art methods.

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“…However, several challenges have also been identified which include 1) the lack of a large scale dataset; 2) the increasing need to integrate more disciplines and agricultural requirements; and 3) ensuring the robustness and accuracy in various complex situations. In another closely related work, Ganesh et al 2019 [15] use mask R-CNN to detect individual fruits and obtain pixel-wise mask for each detected fruit in the image.…”

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confidence: 99%

Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection

Doğru¹,

Bouarfa²,

Arizar³

et al. 2020

Preprint

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Convolutional Neural Networks combined with autonomous drones are increasingly seen as enablers of partially automating the aircraft maintenance visual inspection process. Such an innovative concept can have a significant impact on aircraft operations. Through supporting aircraft maintenance engineers detect and classify a wide range of defects, the time spent on inspection can significantly be reduced. Examples of defects that can be automatically detected include aircraft dents, paint defects, cracks and holes, and lightning strike damage. Additionally, this concept could also increase the accuracy of damage detection and reduce the number of aircraft inspection incidents related to human factors like fatigue and time pressure. In our previous work, we have applied a recent Convolutional Neural Network architecture known by MASK R-CNN to detect aircraft dents. MASK-RCNN was chosen because it enables the detection of multiple objects in an image while simultaneously generating a segmentation mask for each instance. The previously obtained F1 and F2 scores were 62.67% and 59.35% respectively. This paper extends the previous work by applying different techniques to improve and evaluate prediction performance experimentally. The approaches uses include (1) Balancing the original dataset by adding images without dents; (2) Increasing data homogeneity by focusing on wing images only; (3) Exploring the potential of three augmentation techniques in improving model performance namely flipping, rotating, and blurring; and (4) using a pre-classifier in combination with MASK R-CNN. The results show that a hybrid approache combining MASK R-CNN and augmentation techniques leads to an improved performance with an F1 score of (67.50%) and F2 score of (66.37%)

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Deep Orange: Mask R-CNN based Orange Detection and Segmentation

Cited by 112 publications

References 34 publications

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Research on abnormal object detection in specific region based on Mask R-CNN

Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection

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