A Wheat Spike Detection Method in UAV Images Based on Improved YOLOv5

Zhao, Jianqing; Zhang, Xiaohu; Yan, Jiawei; Qiu, Xiaolei; Yao, Xia; Tian, Yongchao; Zhu, Yan; Cao, Weixing

doi:10.3390/rs13163095

Cited by 142 publications

(91 citation statements)

References 55 publications

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“…It can achieve 99.59% segmentation accuracy. Zhao et al (2021) proposed an improved Yolov5 network by adding a microscale detection layer, setting prior anchor boxes, and adapting the confidence loss. These improvement points solve spike error detection and miss detection caused by occlusion conditions in UAV images.…”

Section: Introductionmentioning

confidence: 99%

Wheat Spike Detection and Counting in the Field Based on SpikeRetinaNet

Cheng

Chen

et al. 2022

Front. Plant Sci.

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The number of wheat spikes per unit area is one of the most important agronomic traits associated with wheat yield. However, quick and accurate detection for the counting of wheat spikes faces persistent challenges due to the complexity of wheat field conditions. This work has trained a RetinaNet (SpikeRetinaNet) based on several optimizations to detect and count wheat spikes efficiently. This RetinaNet consists of several improvements. First, a weighted bidirectional feature pyramid network (BiFPN) was introduced into the feature pyramid network (FPN) of RetinaNet, which could fuse multiscale features to recognize wheat spikes in different varieties and complicated environments. Then, to detect objects more efficiently, focal loss and attention modules were added. Finally, soft non-maximum suppression (Soft-NMS) was used to solve the occlusion problem. Based on these improvements, the new network detector was created and tested on the Global Wheat Head Detection (GWHD) dataset supplemented with wheat-wheatgrass spike detection (WSD) images. The WSD images were supplemented with new varieties of wheat, which makes the mixed dataset richer in species. The method of this study achieved 0.9262 for mAP50, which improved by 5.59, 49.06, 2.79, 1.35, and 7.26% compared to the state-of-the-art RetinaNet, single-shot multiBox detector (SSD), You Only Look Once version3 (Yolov3), You Only Look Once version4 (Yolov4), and faster region-based convolutional neural network (Faster-RCNN), respectively. In addition, the counting accuracy reached 0.9288, which was improved from other methods as well. Our implementation code and partial validation data are available at https://github.com/wujians122/The-Wheat-Spikes-Detecting-and-Counting.

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Section: Introductionmentioning

confidence: 99%

Wheat Spike Detection and Counting in the Field Based on SpikeRetinaNet

Cheng

Chen

et al. 2022

Front. Plant Sci.

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“…Architecture of the next YOLO generation—YOLOv5 is very similar to the architecture of the YOLOv4. Although there is no published paper for YOLOv5, but only a repository on GitHub [ 13 ], the YOLOv5 model has been used in many studies [ 54 , 55 ]. Unlike previous versions of YOLO that are written in Darknet framework in the C programming language, YOLOv5 is written in Python which makes installation and integration much easier.…”

Section: Yoloxmentioning

confidence: 99%

Deep Learning Based SWIR Object Detection in Long-Range Surveillance Systems: An Automated Cross-Spectral Approach

Pavlović

Milanović

Stanković

et al. 2022

Sensors

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SWIR imaging bears considerable advantages over visible-light (color) and thermal images in certain challenging propagation conditions. Thus, the SWIR imaging channel is frequently used in multi-spectral imaging systems (MSIS) for long-range surveillance in combination with color and thermal imaging to improve the probability of correct operation in various day, night and climate conditions. Integration of deep-learning (DL)-based real-time object detection in MSIS enables an increase in efficient utilization for complex long-range surveillance solutions such as border or critical assets control. Unfortunately, a lack of datasets for DL-based object detection models training for the SWIR channel limits their performance. To overcome this, by using the MSIS setting we propose a new cross-spectral automatic data annotation methodology for SWIR channel training dataset creation, in which the visible-light channel provides a source for detecting object types and bounding boxes which are then transformed to the SWIR channel. A mathematical image transformation that overcomes differences between the SWIR and color channel and their image distortion effects for various magnifications are explained in detail. With the proposed cross-spectral methodology, the goal of the paper is to improve object detection in SWIR images captured in challenging outdoor scenes. Experimental tests for two object types (cars and persons) using a state-of-the-art YOLOX model demonstrate that retraining with the proposed automatic cross-spectrally created SWIR image dataset significantly improves average detection precision. We achieved excellent improvements in detection performance in various variants of the YOLOX model (nano, tiny and x).

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“…Rotation and randomly clipping images aid detection performance and the robustness of improvement. Luminance changes simulate the deviating brightness of different environmental lighting and improves models' adaptability to different lighting [32]. Some instances of these augmentations are given in Figure 9.…”

Section: Data Augmentation and Labelingmentioning

confidence: 99%

YOLOv4-MN3 for PCB Surface Defect Detection

Liao

et al. 2021

Applied Sciences

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Surface defect detection for printed circuit board (PCB) is indispensable for managing PCB production quality. However, automatic detection of PCB surface defects is still a challenging task because, even within the same category of surface defect, defects present great differences in morphology and pattern. Although many computer vision-based detectors have been established to handle these problems, current detectors struggle to achieve high detection accuracy, fast detection speed and low memory consumption simultaneously. To address those issues, we propose a cost-effective deep learning (DL)-based detector based on the cutting-edge YOLOv4 to detect PCB surface defect quickly and efficiently. The YOLOv4 is improved upon with respect to its backbone network and the activation function in its neck/prediction network. The improved YOLOv4 is evaluated with a customized dataset, collected from a PCB factory. The experimental results show that the improved detector achieved a high performance, scoring 98.64% on mean average precision (mAP) at 56.98 frames per second (FPS), outperforming the other compared SOTA detectors. Furthermore, the improved YOLOv4 reduced the parameter space of YOLOv4 from 63.96 M to 39.59 M and the number of multiply-accumulate operations (Madds) from 59.75 G to 26.15 G.

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A Wheat Spike Detection Method in UAV Images Based on Improved YOLOv5

Cited by 142 publications

References 55 publications

Wheat Spike Detection and Counting in the Field Based on SpikeRetinaNet

Wheat Spike Detection and Counting in the Field Based on SpikeRetinaNet

Deep Learning Based SWIR Object Detection in Long-Range Surveillance Systems: An Automated Cross-Spectral Approach

YOLOv4-MN3 for PCB Surface Defect Detection

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