Rain Rendering and Construction of Rain Vehicle Color-24 Dataset

Hu, Mingdi; Wang, Chenrui; Yang, Jingbing; Wu, Yi; Fan, Jiulun; Jing, Bing-Yi

doi:10.3390/math10173210

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…Therefore, POFs and PGFs have a wide adaptability and application value. There are still many theoretical and applied problems to be studied in terms of pseudo overlap functions and fuzzy logic (see [42][43][44][45][46][47]).…”

Section: Discussionmentioning

confidence: 99%

Pseudo Overlap Functions, Fuzzy Implications and Pseudo Grouping Functions with Applications

Zhang

Liang

Bustince

et al. 2022

Axioms

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Overlap and grouping functions are important aggregation operators, especially in information fusion, classification and decision-making problems. However, when we do more in-depth application research (for example, non-commutative fuzzy reasoning, complex multi-attribute decision making and image processing), we find overlap functions as well as grouping functions are required to be commutative (or symmetric), which limit their wide applications. For the above reasons, this paper expands the original notions of overlap functions and grouping functions, and the new concepts of pseudo overlap functions and pseudo grouping functions are proposed on the basis of removing the commutativity of the original functions. Some examples and construction methods of pseudo overlap functions and pseudo grouping functions are presented, and the residuated implication (co-implication) operators derived from them are investigated. Not only that, some applications of pseudo overlap (grouping) functions in multi-attribute (group) decision-making, fuzzy mathematical morphology and image processing are discussed. Experimental results show that, in many application fields, pseudo overlap functions and pseudo grouping functions have greater flexibility and practicability.

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Section: Discussionmentioning

confidence: 99%

Pseudo Overlap Functions, Fuzzy Implications and Pseudo Grouping Functions with Applications

Zhang

Liang

Bustince

et al. 2022

Axioms

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“…Convolutional neural networks (CNNs) have become wide spread in the last decade. They are most commonly used to solve machine vision tasks in applied science for electronic component classification [1], vehicle identification under rain conditions [2], in agricultural engineering for leaf disease classification [3], efficient beekeeping [4,5], or tree identification from unmanned aerial vehicles [6]. Currently, many workarounds have been proposed to speed-up the training stage and image classification procedure.…”

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of a Convolutional Neural Network and Its Application for Pollen Detection upon Entrance to the Beehive

2022

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The condition of a bee colony can be predicted by monitoring bees upon hive entrance. The presence of pollen grains gives beekeepers significant information about the well-being of the bee colony in a non-invasive way. This paper presents a field-programmable-gate-array (FPGA)-based pollen detector from images obtained at the hive entrance. The image dataset was acquired at native entrance ramps from six different hives. To evaluate and demonstrate the performance of the system, various densities of convolutional neural networks (CNNs) were trained and tested to find those suitable for pollen grain detection at the chosen image resolution. We propose a new CNN accelerator architecture that places a pre-trained CNN on an SoC FPGA. The CNN accelerator was implemented on a cost-optimized Z-7020 FPGA with 16-bit fixed-point operations. The kernel binarization and merging with the batch normalization layer were applied to reduce the number of DSPs in the multi-channel convolutional core. The estimated average performance was 32 GOPS for a single convolutional core. We found that the CNN with four convolutional and two dense layers gave a 92% classification accuracy, and it matched those declared for state-of-the-art methods. It took 8.8 ms to classify a 512 × 128 px frame and 2.4 ms for a 256 × 64 px frame. The frame rate of the proposed method outperformed the speed of known pollen detectors. The developed pollen detector is cost effective and can be used as a real-time image classification module for hive status monitoring.

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“…Fog is a common weather occurrence and can severely damage the image quality captured by the outdoor equipment. There has been a large body of literature on object detection under inclement weather conditions, which include one-stage and combination approaches [1,2]. For example, one-stage approaches include the domain-based adaptive target detection algorithm under foggy conditions [3][4][5][6][7][8]; however, these methods also have limitations, for example, their performance is not guaranteed when the training and test data sets are vastly different, and these methods failed to take advantage of the image recovery potential information while combination ones include single image fog removal [9][10][11][12] and combined object detection [13][14][15][16] algorithms.…”

Section: Introductionmentioning

confidence: 99%

Joint Semantic Deep Learning Algorithm for Object Detection under Foggy Road Conditions

Fan

et al. 2022

Mathematics

Self Cite

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Current mainstream deep learning methods for object detection are generally trained on high-quality datasets, which might have inferior performances under bad weather conditions. In the paper, a joint semantic deep learning algorithm is proposed to address object detection under foggy road conditions, which is constructed by embedding three attention modules and a 4-layer UNet multi-scale decoding module in the feature extraction module of the backbone network Faster RCNN. The algorithm differs from other object detection methods in that it is designed to solve low- and high-level joint tasks, including dehazing and object detection through end-to-end training. Furthermore, the location of the fog is learned by these attention modules to assist image recovery, the image quality is recovered by UNet decoding module for dehazing, and then the feature representations of the original image and the recovered image are fused and fed into the FPN (Feature Pyramid Network) module to achieve joint semantic learning. The joint semantic features are leveraged to push the subsequent network modules ability, and therefore make the proposed algorithm work better for the object detection task under foggy conditions in the real world. Moreover, this method and Faster RCNN have the same testing time due to the weight sharing in the feature extraction module. Extensive experiments confirm that the average accuracy of our algorithm outperforms the typical object detection algorithms and the state-of-the-art joint low- and high-level tasks algorithms for the object detection of seven kinds of objects on road traffics under normal weather or foggy conditions. All codes will be available at github.com/mendy-2013.

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Rain Rendering and Construction of Rain Vehicle Color-24 Dataset

Cited by 8 publications

References 40 publications

Pseudo Overlap Functions, Fuzzy Implications and Pseudo Grouping Functions with Applications

Pseudo Overlap Functions, Fuzzy Implications and Pseudo Grouping Functions with Applications

FPGA Implementation of a Convolutional Neural Network and Its Application for Pollen Detection upon Entrance to the Beehive

Joint Semantic Deep Learning Algorithm for Object Detection under Foggy Road Conditions

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