A detection method of Edge Coherent Mode based on improved SSD

Zeng, Fulin; Liu, Ying; Yang, Yanchao; Zhou, Jiyin; Liu, Xiaotao

doi:10.1016/j.fusengdes.2022.113141

Cited by 9 publications

(2 citation statements)

References 7 publications

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“…Chu et al [19] suppressed non-apple features produced by an improved Mask R-CNN to enhance the detection of apples under varying lighting conditions. On the other hand, one-stage object detection algorithms, which include SSD [20] and YOLO [21], have faster recognition speed while maintaining the same precision as the two-stage object detection algorithms, thereby meeting real-time requirements. For example, Peng et al [22] increased the average detection precision of four fruits by two percentage points by using ResNet-101 to replace the VGG16 network in SSD.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention

et al. 2023

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A tomato automatic detection method based on an improved YOLOv8s model is proposed to address the low automation level in tomato harvesting in agriculture. The proposed method provides technical support for the automatic harvesting and classification of tomatoes in agricultural production activities. The proposed method has three key components. Firstly, the depthwise separable convolution (DSConv) technique replaces the ordinary convolution, which reduces the computational complexity by generating a large number of feature maps with a small amount of calculation. Secondly, the dual-path attention gate module (DPAG) is designed to improve the model’s detection precision in complex environments by enhancing the network’s ability to distinguish between tomatoes and the background. Thirdly, the feature enhancement module (FEM) is added to highlight the target details, prevent the loss of effective features, and improve detection precision. We built, trained, and tested the tomato dataset, which included 3098 images and 3 classes. The proposed algorithm’s performance was evaluated by comparison with the SSD, faster R-CNN, YOLOv4, YOLOv5, and YOLOv7 algorithms. Precision, recall rate, and mAP (mean average precision) were used for evaluation. The test results show that the improved YOLOv8s network has a lower loss and 93.4% mAP on this dataset. This improvement is a 1.5% increase compared to before the improvement. The precision increased by 2%, and the recall rate increased by 0.8%. Moreover, the proposed algorithm significantly reduced the model size from 22 M to 16 M, while achieving a detection speed of 138.8 FPS, which satisfies the real-time detection requirement. The proposed method strikes a balance between model size and detection precision, enabling it to meet agriculture’s tomato detection requirements. The research model in this paper will provide technical support for a tomato picking robot to ensure the fast and accurate operation of the picking robot.

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Section: Introductionmentioning

confidence: 99%

A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention

et al. 2023

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“…The target detection based on deep learning was developed for moving target detection to overcome the limitations of the traditional algorithm. In [2], the authors summed up target detection algorithms based on depth learning, including the faster region convolutional network (Faster R-CNN) algorithm [8][9][10], SSD algorithm [11,12], YOLO [13], YOLOv2 [14], YOLOv3 [15], YOLOv4 [16][17][18][19], YOLOv5 [20] algorithm, etc. It is also pointed out that both YOLO series and single-shot multiBox detector (SSD) algorithms follow the method of R-CNN series algorithms to perform classification pre-training on large datasets, and then fine-tune on small datasets.…”

Section: Introductionmentioning

confidence: 99%

Water Column Detection Method at Impact Point Based on Improved YOLOv4 Algorithm

et al. 2022

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For a long time, the water column at the impact point of a naval gun firing at the sea has mainly depended on manual detection methods for locating, which has problems such as low accuracy, subjectivity and inefficiency. In order to solve the above problems, this paper proposes a water column detection method based on an improved you-only-look-once version 4 (YOLOv4) algorithm. Firstly, the method detects the sea antenna through the Hoffman line detection method to constrain the sensitive area in the current detection image so as to improve the accuracy of water column detection; secondly, density-based spatial clustering of applications with noise (DBSCAN) + K-means clustering algorithm is used to obtain a better prior bounding box, which is input into the YOLOv4 network to improve the positioning accuracy of the water column; finally, the convolutional block attention module (CBAM) is added in the PANet structure to improve the detection accuracy of the water column. The experimental results show that the above algorithm can effectively improve the detection accuracy and positioning accuracy of the water column at the impact point.

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Image Recognition of Agricultural Products Based on the SSD Deep Learning

Wang

2023

Lecture Notes in Electrical Engineering

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A detection method of Edge Coherent Mode based on improved SSD

Cited by 9 publications

References 7 publications

A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention

A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention

Water Column Detection Method at Impact Point Based on Improved YOLOv4 Algorithm

Image Recognition of Agricultural Products Based on the SSD Deep Learning

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