Light-YOLOv3: fast method for detecting green mangoes in complex scenes using picking robots

Xu, Zhi-Feng; Jia, Rui-Sheng; Sun, Hong-Mei; Liu, Qingming; Cui, Zhe

doi:10.1007/s10489-020-01818-w

Cited by 63 publications

(29 citation statements)

References 29 publications

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“…In contrast to the conventional image process method, deep learning can automatically learn the hierarchical feature expression hidden deep in the images, avoiding the tedious procedures to extract and optimize handcrafted features [13]. In the field of agriculture, networks such as Mask R-CNN and YOLO series are usually adopted for target detection and have achieved good effects for mangoes [14,15], strawberries [16], and apples [17]. However, some other models are used for classification such as AlexNet and GoogLeNet.…”

Section: Motivationmentioning

confidence: 99%

Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method

2021

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Accurately extracting the grape cluster at the front of overlapping grape clusters is the primary problem of the grape-harvesting robot. To solve the difficult problem of identifying and segmenting the overlapping grape clusters in the cultivation environment of a trellis, a simple method based on the deep learning network and the idea of region growing is proposed. Firstly, the region of grape in an RGB image was obtained by the finely trained DeepLabV3+ model. The idea of transfer learning was adopted when training the network with a limited number of training sets. Then, the corresponding region of the grape in the depth image captured by RealSense D435 was processed by the proposed depth region growing algorithm (DRG) to extract the front cluster. The depth region growing method uses the depth value instead of gray value to achieve clustering. Finally, it fils the holes in the clustered region of interest, extracts the contours, and maps the obtained contours to the RGB image. The images captured by RealSense D435 in a natural trellis environment were adopted to evaluate the performance of the proposed method. The experimental results showed that the recall and precision of the proposed method were 89.2% and 87.5%, respectively. The demonstrated performance indicated that the proposed method could satisfy the requirements of practical application for robotic grape harvesting.

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Section: Motivationmentioning

confidence: 99%

Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method

2021

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“…Regression-based methods such as SSD (Liu et al, 2016) and YOLO (Redmon et al, 2016) frame take the object detection problem as a regression one, so the object class probability and position coordinates can be directly regressed. The YOLO series algorithm based on regression have fast processing speed and high accuracy, so they have been widely used in actual scenarios, such as fruit detection (Xu et al, 2020), mask-wearing detection (Ren and Liu, 2020), and traffic sign detection (Zhang et al, 2021). YOLOv2 (Redmon and Farhadi, 2017) and YOLOv3 (Li Y. et al, 2019) were improved on the basis of the YOLO algorithm, which further enhances the detection effect.…”

Section: Introductionmentioning

confidence: 99%

“…The experimental results showed that the improved DY3TNet model had small volume and reduced computational complexity, thus realizing real-time detection. Xu et al (2020) improved YOLOv3 by using soft-NMS (nonmaximum suppression) instead of NMS to reduce the loss of the prediction bounding box due to green mango overlap, which can meet the requirements of real-time detection for robotic picking.…”

Section: Introductionmentioning

confidence: 99%

Tomato Anomalies Detection in Greenhouse Scenarios Based on YOLO-Dense

Wang

Liu

2021

Front. Plant Sci.

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Greenhouse cultivation can improve crop yield and quality, and it not only solves people’s daily needs but also brings considerable gains to the agricultural staff. One of the most widely cultivated greenhouse crops is tomato, mainly because of its high nutritional value and its good taste. However, there are a number of anomalies for the tomato crop that pose a threat for its greenhouse cultivation. Detection of tomato anomalies in the complex natural environment is an important research direction in the field of plant science. Automated identification of tomato anomalies is still a challenging task because of its small size and complex background. To solve the problem of tomato anomaly detection in the complex natural environment, a novel YOLO-Dense was proposed based on a one-stage deep detection YOLO framework. By adding a dense connection module in the network architecture, the network inference speed of the proposed model can be effectively improved. By using the K-means algorithm to cluster the anchor box, nine different sizes of anchor boxes with potential objects to be identified were obtained. The multiscale training strategy was adopted to improve the recognition accuracy of objects at different scales. The experimental results show that the mAP and detection time of a single image of the YOLO-Dense network is 96.41% and 20.28 ms, respectively. Compared with SSD, Faster R-CNN, and the original YOLOv3 network, the YOLO-Dense model achieved the best performance in tomato anomaly detection under a complex natural environment.

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“…Networks such as AlexNet and Inception (GoogLeNet) are often used for classification applications such as plant diseases and fruit varieties. Networks such as Mask R-CNN and YOLO are mainly used for target detection and have achieved good effect in the detection applications of mango [21,22], strawberry [23], and apple [24]. Compared with classification and target detection, semantic segmentation can achieve pixel-level segmentation of targets, which is more suitable for our research goals.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Semantic Segmentation Models for Identification of Grape with Different Varieties

et al. 2021

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Accurate fruit segmentation in images is the prerequisite and key step for precision agriculture. In this article, aiming at the segmentation of grape cluster with different varieties, 3 state-of-the-art semantic segmentation networks, i.e., Fully Convolutional Network (FCN), U-Net, and DeepLabv3+ applied on six different datasets were studied. We investigated: (1) the segmentation performance difference of the 3 studied networks; (2) The impact of different input representations on segmentation performance; (3) The effect of image enhancement method to improve the poor illumination of images and further improve the segmentation performance; (4) The impact of the distance between grape clusters and camera on segmentation performance. The experiment results show that compared with FCN and U-Net the DeepLabv3+ combined with transfer learning is more suitable for the task with an intersection over union (IoU) of 84.26%. Five different input representations, namely RGB, HSV, L*a*b, HHH, and YCrCb obtained different IoU, ranging from 81.5% to 88.44%. Among them, the L*a*b got the highest IoU. Besides, the adopted Histogram Equalization (HE) image enhancement method could improve the model’s robustness against poor illumination conditions. Through the HE preprocessing, the IoU of the enhanced dataset increased by 3.88%, from 84.26% to 88.14%. The distance between the target and camera also affects the segmentation performance, no matter in which dataset, the closer the distance, the better the segmentation performance was. In a word, the conclusion of this research provides some meaningful suggestions for the study of grape or other fruit segmentation.

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Light-YOLOv3: fast method for detecting green mangoes in complex scenes using picking robots

Cited by 63 publications

References 29 publications

Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method

Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method

Tomato Anomalies Detection in Greenhouse Scenarios Based on YOLO-Dense

A Comparative Study of Semantic Segmentation Models for Identification of Grape with Different Varieties

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