Automatic Pear Extraction from High-Resolution Images by a Visual Attention Mechanism Network

Jinjie, Wang; Ding, Jianli; Ran, Si; Qin, Shaofeng; Liu, Bohua; Li, Xiang

doi:10.3390/rs15133283

Cited by 3 publications

(1 citation statement)

References 68 publications

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“…With the rapid development of image processing, high-resolution sensors, chips and artificial intelligence, IGT has been widely studied and developed [6,7]. IGT can obtain rich imaging information with multi band, high sensitivity and high resolution, which helps in finding and identifying objects in a complex background [8,9].…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification of Pilot Model and Stability Analysis of Human-in-Loop Image Seeker

Zhang,

Li,

et al. 2023

Aerospace

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In the human-in-loop (HIL) guidance mode, a pilot quickly identifies and flexibly locks on to a target through a real-time image signal transmitted by the aircraft. Then, the line-of-sight (LOS) angle error in the viewing field is tracked and compensated for in order to improve the guidance and control performance of the image-guided aircraft. Based on the physical structure and device parameters of the image seeker, an appropriate correction network is designed to improve the performance of the seeker stability loop. Aiming at a precise-extended crossover (PEC) pilot model, the structure of the dynamic model is optimized, and the maximum likelihood estimation (MLE) method of the output error structure is used to identify the dynamic parameters. This makes up for the deficiency of the existing modeling. In order to solve the nonlinear optimization problems encountered in the identification process, a hybrid strategy of a genetic algorithm (GA) and Gauss–Newton optimization algorithm is used to improve the probability of finding the global optimal solution. The simplex method is also used to improve the robustness of the algorithm. In addition, a hardware-in-the-loop simulation is designed and multi-round HIL experiment flow is performed. Moreover, based on the adaptability of the pilot to different image signal delays, the effects of different image signal delays on the stability and disturbance rejection rate (DRR) of the seeker control system are studied. The results demonstrate that the hybrid gradient optimization algorithm (HGOA) can find the global optimal value, and the identification model can accurately reflect the dynamic characteristics of the pilot. In the HIL guidance mode, the tracking compensation behavior of the pilot can reduce the influence of image signal delay on the disturbance of the aircraft body isolated by the seeker. The optimized PEC model and the identified dynamic parameters improve the efficiency of pilot training and screening.

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Section: Introductionmentioning

confidence: 99%

Parameter Identification of Pilot Model and Stability Analysis of Human-in-Loop Image Seeker

Zhang,

Li,

et al. 2023

Aerospace

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Application of an improved U-Net with image-to-image translation and transfer learning in peach orchard segmentation

Cheng,

Zhu,

Zhao

et al. 2024

International Journal of Applied Earth Observation and Geoinfor

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Detection and Mapping of Chestnut Using Deep Learning from High-Resolution UAV-Based RGB Imagery

Sun,

Hao,

Guo

et al. 2023

Remote Sensing

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The semantic segmentation method based on high-resolution RGB images obtained by unmanned aerial vehicle (UAV) provides a cost-effective way to improve the accuracy of detection and classification in forestry. Few studies have explored the impact of sample distribution patterns on deep learning model detection accuracy. The study was carried out using the data from the 4.78 km2 RGB image of a chestnut (Castanea mollissima Blume) plantation obtained by the DJI Phantom 4-RTK, and the model training was conducted with 18,144 samples of manually delineated chestnut tree clusters. The performance of four semantic segmentation models (U-Net, DeepLab V3, PSPNet, and DeepLab V3+) paired with backbones (ResNet-34, ResNet-50) was evaluated. Then, the influence of chestnut data from different planting patterns on the accuracy and generalization performance of deep learning models was examined. The results showed that the combination of DeepLab V3 with ResNet-34 backbone gives the best performance (F1 score = 86.41%), while the combination of DeepLab V3+ with ResNet-50 backbone performed the worst. The influence of different backbone networks on the detection performance of semantic segmentation models did not show a clear pattern. Additionally, different spatial distribution patterns of chestnut planting affected the classification accuracy. The model MIX, trained on comprehensive training data, achieves higher classification accuracies (F1 score = 86.13%) compared to the model trained on single training data (F1 score (DP) = 82.46%; F1 score (SP) = 83.81%). The model performance in complex scenario data training is superior to that of the model in simple scene data training. In conclusion, comprehensive training databases can improve the generalization performance of chestnut classification with different spatial distribution patterns. This study provides an effective method for detecting chestnut cover area based on semantic segmentation, allowing for better quantitative evaluation of its resource utilization and further development of inventories for other tree species.

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Automatic Pear Extraction from High-Resolution Images by a Visual Attention Mechanism Network

Cited by 3 publications

References 68 publications

Parameter Identification of Pilot Model and Stability Analysis of Human-in-Loop Image Seeker

Parameter Identification of Pilot Model and Stability Analysis of Human-in-Loop Image Seeker

Application of an improved U-Net with image-to-image translation and transfer learning in peach orchard segmentation

Detection and Mapping of Chestnut Using Deep Learning from High-Resolution UAV-Based RGB Imagery

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