Cascade and Fusion: A Deep Learning Approach for Camouflaged Object Sensing

Huang, Kaixun; Li, Chunshu; Zhang, Jiaqi; Wang, Beilun

doi:10.3390/s21165455

Cited by 12 publications

(12 citation statements)

References 49 publications

(99 reference statements)

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“…This review includes articles with Qualsyst score percentages ≥60%. As a result, 23 articles were included for in-depth analysis [ 7 , 13 , 14 , 15 , 16 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ]; the remaining 5 articles were excluded since their score was below the acceptance percentage [ 45 , 46 , 47 , 48 , 49 ]. The flow diagram of the review phases’ is shown in Figure 2 .…”

Section: Resultsmentioning

confidence: 99%

Machine Learning-Based Sensor Data Fusion for Animal Monitoring: Scoping Review

Aguilar-Lazcano

Espinosa-Curiel

Ríos-Martínez

et al. 2023

Sensors

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The development of technology, such as the Internet of Things and artificial intelligence, has significantly advanced many fields of study. Animal research is no exception, as these technologies have enabled data collection through various sensing devices. Advanced computer systems equipped with artificial intelligence capabilities can process these data, allowing researchers to identify significant behaviors related to the detection of illnesses, discerning the emotional state of the animals, and even recognizing individual animal identities. This review includes articles in the English language published between 2011 and 2022. A total of 263 articles were retrieved, and after applying inclusion criteria, only 23 were deemed eligible for analysis. Sensor fusion algorithms were categorized into three levels: Raw or low (26%), Feature or medium (39%), and Decision or high (34%). Most articles focused on posture and activity detection, and the target species were primarily cows (32%) and horses (12%) in the three levels of fusion. The accelerometer was present at all levels. The findings indicate that the study of sensor fusion applied to animals is still in its early stages and has yet to be fully explored. There is an opportunity to research the use of sensor fusion for combining movement data with biometric sensors to develop animal welfare applications. Overall, the integration of sensor fusion and machine learning algorithms can provide a more in-depth understanding of animal behavior and contribute to better animal welfare, production efficiency, and conservation efforts.

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Section: Resultsmentioning

confidence: 99%

Machine Learning-Based Sensor Data Fusion for Animal Monitoring: Scoping Review

Aguilar-Lazcano

Espinosa-Curiel

Ríos-Martínez

et al. 2023

Sensors

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“…The loss function that we use during training is a pixel frequency aware loss, 38 which mainly consists of a weighted binary cross-entropy (wBCE) and a weighted intersection-over-union (wIoU). wBCE is expressed as LwBCE=−∑x=1H∑y=1W(1+αω(x,y))log P(p(x,y)=g(x,y)|φ)∑x=1H∑y=1Wαω(x,y).wIoU is written as LwIoU=1−∑x=1H∑y=1W[g(x,y)×p(x,y)]×(1+αω(x,y))…”

Section: Resultsmentioning

confidence: 99%

“…The loss function that we use during training is a pixel frequency aware loss, 38 which mainly consists of a weighted binary cross-entropy (wBCE) and a weighted intersection-over-union (wIoU). wBCE is expressed as E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 2 ; 1 1 6 ; 3 1 8…”

Section: Training Implementationmentioning

confidence: 99%

EINet: camouflaged object detection with pyramid vision transformer

Jin

2022

J. Electron. Imag.

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Camouflaged object detection (COD) is a new computer vision challenge for locating and identifying camouflaged objects in complex situations. Camouflaged objects are more similar to their surroundings than conventional objects, and their appearance in terms of size and shape is also considerably different, making accurate identification of the COD tasks difficult. As a result, we propose an enhanced identification network (EINet) to strengthen the COD task's identification capabilities. First, the pyramid vision transformer is used as an encoder for extracting more robust multiscale features. Second, the multiple texture refinement modules are exploited to refine the multiscale features. Third, an improved neighbor and hop connection decoder is designed to produce a coarse estimation map for guiding the detailed identification of camouflaged objects backward. Finally, numerous new reverse criss-cross block attention modules that gradually recognize fine-grained features at various scales is designed to allow for the accurate recognition of camouflaged objects. Extensive experiments have been conducted on four benchmarked datasets of camouflaged objects. The results of the experiments reveal that our EINet is a powerful COD model that outperforms current state-of-the-art models.

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“…Multi-scale feature fusion is a technique for combining feature maps at different scales to improve the performance of computer vision tasks. Common approaches include cascade structures [ 36 , 37 , 38 ], pyramid networks [ 39 , 40 , 41 , 42 ] and attention mechanisms [ 43 , 44 , 45 ]. Cascade structures link feature maps at different scales together to form cascade networks, which can be effective in improving performance; pyramid networks are a hierarchical approach to image processing that extracts features at different scales and combines them; and attention mechanisms can make the network focus more on important features by weighting feature maps at different scales.…”

Section: Related Workmentioning

confidence: 99%

MFF-YOLO: An Accurate Model for Detecting Tunnel Defects Based on Multi-Scale Feature Fusion

Жу

Wang

Xie

et al. 2023

Sensors

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Tunnel linings require routine inspection as they have a big impact on a tunnel’s safety and longevity. In this study, the convolutional neural network was utilized to develop the MFF-YOLO model. To improve feature learning efficiency, a multi-scale feature fusion network was constructed within the neck network. Additionally, a reweighted screening method was devised at the prediction stage to address the problem of duplicate detection frames. Moreover, the loss function was adjusted to maximize the effectiveness of model training and improve its overall performance. The results show that the model has a recall and accuracy that are 7.1% and 6.0% greater than those of the YOLOv5 model, reaching 89.5% and 89.4%, respectively, as well as the ability to reliably identify targets that the previous model error detection and miss detection. The MFF-YOLO model improves tunnel lining detection performance generally.

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Cascade and Fusion: A Deep Learning Approach for Camouflaged Object Sensing

Cited by 12 publications

References 49 publications

Machine Learning-Based Sensor Data Fusion for Animal Monitoring: Scoping Review

Machine Learning-Based Sensor Data Fusion for Animal Monitoring: Scoping Review

EINet: camouflaged object detection with pyramid vision transformer

MFF-YOLO: An Accurate Model for Detecting Tunnel Defects Based on Multi-Scale Feature Fusion

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