Few-Shot Object Detection Based on Self-Knowledge Distillation

Li, Yang; Gong, Yicheng; Zhang, Zhuo

doi:10.1109/mis.2022.3205686

Cited by 18 publications

(6 citation statements)

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“…[14][15][16] The object detection has become increasingly mature, moving gradually from pure theoretical research to implementation. [17][18][19][20] Due to the high performance and real-time performance of deep learning algorithms, many researchers apply it to pest detection to better protect crops.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, many object detection algorithms have been proposed, the most representative of which are the one-stage method YOLO series, 9 – 12 SSD, 13 and the two-stage method R-CNN series 14 – 16 The object detection has become increasingly mature, moving gradually from pure theoretical research to implementation 17 – 20 Due to the high performance and real-time performance of deep learning algorithms, many researchers apply it to pest detection to better protect crops.…”

Section: Introductionmentioning

confidence: 99%

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Data-driven few-shot crop pest detection based on object pyramid for smart agriculture

Xiao

Kumar

et al. 2023

J. Electron. Imag.

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.Farmers are often annoyed by crop pests, which can lead to serious losses if not detected in time. Deep learning has gradually penetrated into the agricultural field due to its proven superior performance, promoting the development of smart agriculture. Its real-time performance and effectiveness can help farmers spot pests in time and reduce losses. However, with the increasing application of deep learning technology, its disadvantage of relying heavily on large amounts of data has gradually been exposed. Before training a network, people need to spend a lot of manpower, time, and money to prepare a complete dataset and make the correct labels. To solve this problem, we introduced a less-shot object detection method in crop pest detection. We provide the first few-shot pest detection dataset (FSPD), which contains 20 categories and 2000 samples for this research. We use a few-shot object detection network based on an object pyramid called multiscale positive sample refinement. Numerous experiments are conducted to prove that, with only few (1/2/3/5/10-shot) samples, the accuracy of our method on FSPD is better than that of traditional deep learning methods, such as YOLO and faster R-CNN. After embedding the object pyramid branch, the network performance improves by an average of 5.9% on three splits compared with that without embedding. With input of only 10-shot novel samples, our method can achieve 76%, 79%, and 82% for the mAP on three splits, respectively. As a research study on the detection of crop diseases and pests with less radiation, our work can give some new insight to the future scholars, which is of great significance to the development of smart agriculture.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-driven few-shot crop pest detection based on object pyramid for smart agriculture

Xiao

Kumar

et al. 2023

J. Electron. Imag.

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“…The manually designed features were difficult to migrate. So it was inevitable to require redesigning features for different tasks in different images, which was labor-intensive and costly [14].…”

Section: Introductionmentioning

confidence: 99%

CFNet: An Eigenvalue Preserved Approach to Multiscale Building Segmentation in High-Resolution Remote Sensing Images

Liu

Bilal

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In recent years, AI and Deep Learning (DL) methods have been widely used for object classification, recognition, and segmentation of high-resolution multispectral remote sensing images. These DL-based solutions perform better compare to traditional spectral algorithms but still suffer from insufficient optimization of global and local features of object context. In addition, failure of code-data isolation and/or disclosure of detailed eigenvalues causes serious privacy and even secret leakage due to the sensitivity of high-resolution remote sensing data and their processing mechanisms. In this paper, Class Feature (CF) modules have been presented in the decoder part of an attention-based CNN network to distinguish between building and non-building (background) area. In this way, context features of a focused object can be extracted with more details being processed, whilst the resolution of images is maintained. The reconstructed local and global feature values and dependencies in the proposed model are maintained by reconfiguring multiple effective attention modules with contextual dependencies to achieve better results for the eigenvalue. According to quantitative results and their visualization, the proposed model has depicted better performance over others' work using two largescale building remote sensing datasets. The F1-score of this model reached 87.91 and 89.58 on WHU Buildings Dataset and Massachusetts Buildings Dataset, respectively, which exceeded the other semantic segmentation models.

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“…7 The intelligent weed identification algorithm based on the convolutional neural network 8 has made some achievements in the production and life of intelligent agriculture and has made contributions to the disinfection and sterilization of weeds. The current computer vision research on weed disinfection and sterilization activities is generally weed image classification recognition, 9 target detection, 10 and data prediction. 11 In the field of image recognition, convolution neural networks have been proposed.…”

Section: Introductionmentioning

confidence: 99%

High-information data-centric weed images data identification system based on triple filter for smart agriculture

Zhang

2023

J. Electron. Imag.

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Weeds have seriously impacted crop planting and caused serious human losses.The intelligent weed identification algorithm based on the convolutional neural network has made some achievements in intelligent agriculture. However, the current results rely on a large number of labeled weed data, which is costly to obtain. How to effectively identify without big data is a key task in the current field. We designed a high-information data-centric weed images data identification system based on a triple filter. The system consists of three modules: nearest-neighbor core metric, unobserved components model, and outlier detection. Extensive scientific experiments have shown that our system requires only a small amount of data to achieve excellent performance in weed identification tasks. Our system saves 5% to 20% of the data. When using only 80% of the data, our system achieves the model performance obtained with 100% of data training. Compared with other methods, this method improves the accuracy by 4.9% and reaches a state-of-the-art performance. Our work solves the problem of relying on image data in smart agriculture, provides a scheme for weed identification tasks, and provides valuable ideas for future intelligent agricultural research.

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Few-Shot Object Detection Based on Self-Knowledge Distillation

Cited by 18 publications

References 0 publications

Data-driven few-shot crop pest detection based on object pyramid for smart agriculture

Data-driven few-shot crop pest detection based on object pyramid for smart agriculture

CFNet: An Eigenvalue Preserved Approach to Multiscale Building Segmentation in High-Resolution Remote Sensing Images

High-information data-centric weed images data identification system based on triple filter for smart agriculture

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