Fast Implementation of Insect Multi-Target Detection Based on Multimodal Optimization

Wang, Rui; Zhang, Yiming; Tian, Weiming; Cai, Jiong; Hu, Cheng; Zhang, Tianran

doi:10.3390/rs13040594

Cited by 3 publications

(5 citation statements)

References 32 publications

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“…Table 1 is the detailed comparison of experimental results, where 1 represents the use of ResNet-BiFPN improved feature extraction network on Faster-RCNN alone. 2 represents the use of EIoU alone on Faster-RCNN, and 3 represents the use of K-means++ on Faster-RCNN. 1 + 2 , 2 + 3 , and 1 + 3 represent the combination of two improvements corresponding to numbers, and the proposed is the improved Faster-RCNN underwater organism detection algorithm proposed in this paper.…”

Section: Resultsmentioning

confidence: 99%

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Underwater Biological Detection Algorithm Based on Improved Faster-RCNN

Shi

et al. 2021

Water

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Underwater organisms are an important part of the underwater ecological environment. More and more attention has been paid to the perception of underwater ecological environment by intelligent means, such as machine vision. However, many objective reasons affect the accuracy of underwater biological detection, such as the low-quality image, different sizes or shapes, and overlapping or occlusion of underwater organisms. Therefore, this paper proposes an underwater biological detection algorithm based on improved Faster-RCNN. Firstly, the ResNet is used as the backbone feature extraction network of Faster-RCNN. Then, BiFPN (Bidirectional Feature Pyramid Network) is used to build a ResNet–BiFPN structure which can improve the capability of feature extraction and multi-scale feature fusion. Additionally, EIoU (Effective IoU) is used to replace IoU to reduce the proportion of redundant bounding boxes in the training data. Moreover, K-means++ clustering is used to generate more suitable anchor boxes to improve detection accuracy. Finally, the experimental results show that the detection accuracy of underwater biological detection algorithm based on improved Faster-RCNN on URPC2018 dataset is improved to 88.94%, which is 8.26% higher than Faster-RCNN. The results fully prove the effectiveness of the proposed algorithm.

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Section: Resultsmentioning

confidence: 99%

“…It deals with specific types of semantic objects (such as people, cars, or animals) in digital images or videos. The research fields of target detection include edge detection [1], multi-target detection [2][3][4], salient target detection [5,6], and so on.…”

Section: Introductionmentioning

confidence: 99%

Underwater Biological Detection Algorithm Based on Improved Faster-RCNN

Shi

et al. 2021

Water

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“…Firstly, the target set is divided based on the validation matrix defined in (9). Assume that n j is the number of validation gates that measurement j lies in, i.e., n j = n t ∑ t=1 ω jt .…”

Section: Distance-correction Factor Calculationmentioning

confidence: 99%

“…Meanwhile, the tracking of insect targets via high-resolution radar is of great significance for the study of insect migration mechanisms [8]. However, for the insect target tracking in cluttered environments [9], complex flight trajectories (such as parallel and crossing trajectories) and large numbers of false measurements not only make it difficult to accurately determine the sources of the measurements but also increase the calculation burden.…”

Section: Introductionmentioning

confidence: 99%

Design of Insect Target Tracking Algorithm in Clutter Based on the Multidimensional Feature Fusion Strategy

et al. 2021

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Entomological radar is an effective means of monitoring insect migration, and can realize long-distance and large-scale rapid monitoring. The stable tracking of individual insect targets is the basic premise underlying the identification of insect species and the study of insect migration mechanisms. However, the complex motion trajectory and large number of false measurements decrease the performance of insect target tracking. In this paper, an insect target tracking algorithm in clutter was designed based on the multidimensional feature fusion strategy (ITT-MFF). Firstly, multiple feature parameters of measurements were fused to calculate the membership of measurements and target, thereby improving the data association accuracy in the presence of clutter. Secondly, a distance-correction factor was introduced to the probabilistic data association (PDA) algorithm to accomplish multi-target data association with a low computational cost. Finally, simulation scenarios with different target numbers and clutter densities were constructed to verify the effectiveness of the proposed method. The tracking result comparisons of the experimental data acquired from a Ku-band entomological radar also indicate that the proposed method can effectively reduce computational cost while maintaining high tracking precision, and is suitable for engineering implementation.

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“…Hence, its precision depends on the effective observation of the flight track, which includes the high-resolution measurement of the insects' height and the effective detection of the tiny insects from the background noise. For the detection of insects, several methods have been proposed and proven their robustness in operation [19][20][21][22][23][24]. For the high-resolution measurement of the insects, the current VLRs are all non-coherent and emit unmodulated pulses with the pulse width of 0.05 µs, 0.08 µs, or 0.1 µs, corresponding to the range resolution of 7.5, 12, or 15 m respectively [18,22,25].…”

Section: Introductionmentioning

confidence: 99%

High-Resolution and Low Blind Range Waveform for Migratory Insects’ Taking-Off and Landing Behavior Observation

et al. 2022

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The observation of taking-off and landing behaviors of migratory insects is important for pest early monitoring and forecasting. Entomological radar, which can measure the ascent or descent rates remotely, has been proved to be the most effective way to observe the above behaviors. For the current entomological radars, the highest range resolution of 7.5 m and smallest blind range of 150 m make it difficult to distinguish individual insects in a swarm and observe the migratory behaviors at low altitudes. In this paper, based on the outfield data acquired from a high-resolution radar of 0.2 m, the spatial spacing distribution of migratory insects is presented for the first time and waveform design requirements are analyzed, proving the necessity of a higher resolution. Secondly, for the high-resolution and low blind range observation of insects, the stepped frequency train of LFM pulses (also called frequency-jumped burst, FJB) waveform is discussed as the optimal choice. In order to resolve the high grating-lobe problem in the FJB waveform with the low blind range, the precise spectrum model and its approximation of the LFM subpulse are first derived in detail. Then, according to the obtained spectrum characteristics, the high-resolution and low blind range FJB waveform design methods based on spectrum fluctuation period and Fresnel integral windowing are proposed to reduce the grating-lobe number to more than 50% and suppress the highest grating lobe level by at least 4 dB. Finally, based on the high-resolution and low blind range VLR adopting the proposed waveform, several typical taking-off and landing behavior observation results are presented, which proves that, in their migratory pattern, insects usually take off around sunset.

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Fast Implementation of Insect Multi-Target Detection Based on Multimodal Optimization

Cited by 3 publications

References 32 publications

Underwater Biological Detection Algorithm Based on Improved Faster-RCNN

Underwater Biological Detection Algorithm Based on Improved Faster-RCNN

Design of Insect Target Tracking Algorithm in Clutter Based on the Multidimensional Feature Fusion Strategy

High-Resolution and Low Blind Range Waveform for Migratory Insects’ Taking-Off and Landing Behavior Observation

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