Combining SUN-based visual attention model and saliency contour detection algorithm for apple image segmentation

Wang, Dandan; He, Dongjian; Song, Huaibo; Liu, Chang; Xiong, Hongting

doi:10.1007/s11042-018-7106-y

Cited by 21 publications

(8 citation statements)

References 35 publications

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“…Additionally, the detection of smaller objects behind is difficult. In the case of smooth light, the light intensity of part of the surface of the apple becomes higher, and the outer surface shows a daytime color without the characteristics of veins [21].…”

Section: Recognition Of Different Lighting Angles By Differentmentioning

confidence: 99%

[Retracted] Fast Recognition Method for Multiple Apple Targets in Complex Occlusion Environment Based on Improved YOLOv5

2023

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The mechanization and intelligentization of the production process are the main trends in research and development of agricultural products. The realization of an unmanned and automated picking process is also one of the main research hotspots in China’s agricultural product engineering technology field in recent years. The development of automated apple-picking robot is directly related to imaging research, and its key technology is to use algorithms to realize apple identification and positioning. Aiming at the problem of false detection and missed detection of densely occluded targets and small targets by apple picking robots under different lighting conditions, two different apple recognition algorithms are selected based on the apple shape features to study the traditional machine learning algorithm: histogram of oriented gradients + support vector machine (HOG + SVM) and a fast recognition method for multiple apple targets in a complex occlusion environment based on improved You-Only-Look-Once-v5 (YOLOv5). The first is the improvement of the CSP structure in the network. Using parameter reconstruction, the convolutional layer (Conv) and the batch normalization (BN) layer in the CBL (Conv + BN + Leaky_relu activation function) module are fused into a batch-normalized convolutional layer Conv_B. Subsequently, the CA (coordinate attention) mechanism module is embedded into different network layers in the improved designed backbone network to enhance the expressive ability of the features in the backbone network to better extract the features of different apple targets. Finally, for some targets with overlapping occlusions, the loss function is fine-tuned to improve the model’s ability to recognize occluded targets. By comparing the recognition effects of HOG + SVM, Faster RCNN, YOLOv6, and baseline YOLOv5 on the test set under complex occlusion scenarios, the F 1 value of this method was increased by 13.47%, 6.01%, 1.26%, and 3.63%, respectively, and the F 1 value of this method was increased by 19.36%, 13.07%, 1.61%, and 4.27%, respectively, under different illumination angles. The average image recognition time was 0.27 s faster than that of HOG + SVM, 0.229 s faster than that of Faster RCNN, and 0.006 s faster than that of YOLOv6. The method is expected to provide a theoretical basis for apple-picking robots to choose a pertinent image recognition algorithm during operation.

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Section: Recognition Of Different Lighting Angles By Differentmentioning

confidence: 99%

[Retracted] Fast Recognition Method for Multiple Apple Targets in Complex Occlusion Environment Based on Improved YOLOv5

2023

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“…The image

calculates the gradient image

obtained under the new color space using the morphological minimum calibration technology [ 34 , 35 , 36 , 37 , 38 , 39 ]. It can correct the original local minimum value of the

image so that the minimum value retained in the modified gradient image corresponding to the area of 1 in the labeled image.…”

Section: Methodsmentioning

confidence: 99%

The Algorithm of Watershed Color Image Segmentation Based on Morphological Gradient

2022

Sensors

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The traditional watershed algorithm has the disadvantage of over-segmentation and interference with an image by reflected light. We propose an improved watershed color image segmentation algorithm. It is based on a morphological gradient. This method obtains the component gradient of a color image in a new color space is not disturbed by the reflected light. The gradient image is reconstructed by opening and closing. Therefore, the final gradient image is obtained. The maximum inter-class variance algorithm is used to obtain the threshold automatically for the final gradient image. The original gradient image is forcibly calibrated with the obtained binary labeled image, and the modified gradient image is segmented by watershed. Experimental results show that the proposed method can obtain an accurate and continuous target contour. It will achieve the minimum number of segmentation regions following human vision. Compared with similar algorithms, this way can suppress the meaningless area generated by the reflected light. It will maintain the edge information of the object well. It will improve the robustness and applicability. From the experimental results, it can be seen that compared with the region-growing method and the automatic threshold method; the proposed algorithm has a great improvement in operation efficiency, which increased by 10%. The accuracy and recall rate of the proposed algorithm is more than 0.98. Through the experimental comparison, the advantages of the proposed algorithm in object segmentation can be more intuitively illustrated.

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“…Equations ( 9) and (10) show that the theoretical acceleration ratio is equal to the model compression ratio and that the Ghost module reduces the amount of convolution computation to a certain extent while maintaining the detection performance. As a result, smaller models can be obtained.…”

Section: ) Ghost Modulementioning

confidence: 99%

TDPPL-Net: A Lightweight Real-Time Tomato Detection and Picking Point Localization Model for Harvesting Robots

Song

Wang

et al. 2023

IEEE Access

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Existing target detection models are large and have multiple network parameters, which can severely slow down the detection speed when deployed on small, low-cost GPU-free Industrial Personal Computers (IPC). As a result, this study proposes a lightweight real-time tomato detection and point-picking integrated network model based on YOLOv5 (TDPPL-Net). Firstly, the algorithm replaces the YOLOv5 backbone with a four-group lightweight downsampling model consisting of Ghost Conv and Ghost Bottleneck to reduce the model size, while adding the attention mechanism SimAM module to improve detection accuracy after each scale's feature map. Secondly, the Spatial Pyramid Pooling-Fast(SPPF) network structure is used and the convolutional layers in the (Feature Pyramid Network and Path Aggregation Network)FPN+PAN structure are replaced with a depth-separable convolution to reduce the computational effort. Finally, the center of the bounding box is used as the picking point, and the corresponding depth information is obtained in combination with the Intel RealSense D435 camera, which is converted into 3D coordinates under the robot arm coordinate system after hand-eye calibration. The experimental results show that TDPPL-Net reduces the number of parameters by 59.84% compared with the original YOLOv5, the model volume is only 40% of the original, the mAP is 93.36, and the real-time detection speed on the IPC without GPU acceleration is 31.41 FPS, which is 170.31% higher than YOLOv5. The TDPPL-Net increases detection speed on low-performance equipment without compromising detection accuracy. It can detect and locate tomato picking points in real-time in the complex natural environment, which can meet the working requirements of harvesting robots.INDEX TERMS Lightweight, Real-time tomato detection, Location of picking points, Harvesting robot.

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Combining SUN-based visual attention model and saliency contour detection algorithm for apple image segmentation

Cited by 21 publications

References 35 publications

[Retracted] Fast Recognition Method for Multiple Apple Targets in Complex Occlusion Environment Based on Improved YOLOv5

[Retracted] Fast Recognition Method for Multiple Apple Targets in Complex Occlusion Environment Based on Improved YOLOv5

The Algorithm of Watershed Color Image Segmentation Based on Morphological Gradient

TDPPL-Net: A Lightweight Real-Time Tomato Detection and Picking Point Localization Model for Harvesting Robots

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