A deep learning method for oriented and small wheat spike detection (OSWSDet) in UAV images

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

doi:10.1016/j.compag.2022.107087

Cited by 31 publications

(24 citation statements)

References 31 publications

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“…Adjusting network structure will affect object detection accuracy for a deep learning network [ 35 ]. Based on subjective experience, researchers have enhanced the detection network’s performance by adding a micro-scale detection layer [ 36 ], adjusting feature enhancement modules [ 37 – 43 ], and rotating original horizontal detection boxes [ 44 – 46 ]. However, the studies mentioned above focused merely on the direct application of prior knowledge and thus lacked significant support from interpretive works [ 47 ].…”

Section: Discussionmentioning

confidence: 99%

Improving multi-scale detection layers in the deep learning network for wheat spike detection based on interpretive analysis

et al. 2023

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Background Detecting and counting wheat spikes is essential for predicting and measuring wheat yield. However, current wheat spike detection researches often directly apply the new network structure. There are few studies that can combine the prior knowledge of wheat spike size characteristics to design a suitable wheat spike detection model. It remains unclear whether the complex detection layers of the network play their intended role. Results This study proposes an interpretive analysis method for quantitatively evaluating the role of three-scale detection layers in a deep learning-based wheat spike detection model. The attention scores in each detection layer of the YOLOv5 network are calculated using the Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm, which compares the prior labeled wheat spike bounding boxes with the attention areas of the network. By refining the multi-scale detection layers using the attention scores, a better wheat spike detection network is obtained. The experiments on the Global Wheat Head Detection (GWHD) dataset show that the large-scale detection layer performs poorly, while the medium-scale detection layer performs best among the three-scale detection layers. Consequently, the large-scale detection layer is removed, a micro-scale detection layer is added, and the feature extraction ability in the medium-scale detection layer is enhanced. The refined model increases the detection accuracy and reduces the network complexity by decreasing the network parameters. Conclusion The proposed interpretive analysis method to evaluate the contribution of different detection layers in the wheat spike detection network and provide a correct network improvement scheme. The findings of this study will offer a useful reference for future applications of deep network refinement in this field.

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Section: Discussionmentioning

confidence: 99%

Improving multi-scale detection layers in the deep learning network for wheat spike detection based on interpretive analysis

et al. 2023

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“…In order to evaluate the performance of the crack detection model more intuitively, this paper introduced the precision rate (Pr) of crack identification, positioning and recall rate (Re). Pr and Re are defined in Formula (), () [27]:

\begin{equation} {\rm{Pr}} = \frac{\rm{TP}}{\rm{TP + FP}}, \end{equation}

\begin{equation} {\rm{Re}} = \frac{\rm{TP}}{\rm{TP + FN}}. \end{equation}

…”

Section: Methodsmentioning

confidence: 99%

Section: Evaluation Criteriamentioning

confidence: 99%

Bridge bottom crack detection and modeling based on faster R‐CNN and BIM

Gan,

Liu,

Yan

et al. 2023

IET Image Processing

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The bridge bottom crack detection provides important state information for bridge disease control and safety assessment. This paper proposes a detection method based on deep learning Faster R‐CNN and BIM (Building Information Modeling). The UAV (Unmanned Aerial Vehicle) was used for close aerial photography to obtain high‐resolution crack images of the concrete surface at the bottom of a bridge. Through deep learning algorithms, a Faster R‐CNN model is trained and established for crack identifications. The crack identification accuracy rate and recall rate reach 92.03% and 96.54%, respectively. Crack images are mapped to a BIM model developed for the chosen bridge, and the box girder family with cracks and the three crack families of transverse cracks, longitudinal cracks and turtle cracks are established. The cracks are located and the visualization of the beam bridge with cracks was completed. In order to better assess the health condition of the bridge. The results show that the combination of UAV bridge crack detection and modelling solves the remote, visual and automated detection of cracks on the surface of bridge structures, which are difficult to reach manually, and has important scientific research and engineering application value.

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“…Although the average recognition accuracy value was as high as 93.4% for wheat-ear counting, it was unavailable for real-time detection owing to the slow pace of detection. Zhao et al [ 7 ] proposed a deep neural network-based wheat-ear-detection method with an AP (average precision) value of 94.5% for real-time detection results. David et al [ 8 ] established a global wheat head detection (GWHD) dataset in 2021, which has the advantage of producing less noise and assessing more samples compared to GWHD_2020.…”

Section: Introductionmentioning

confidence: 99%

Research on the Method of Counting Wheat Ears via Video Based on Improved YOLOv7 and DeepSort

Zhong

Chen

et al. 2023

Sensors

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The number of wheat ears in a field is an important parameter for accurately estimating wheat yield. In a large field, however, it is hard to conduct an automated and accurate counting of wheat ears because of their density and mutual overlay. Unlike the majority of the studies conducted on deep learning-based methods that usually count wheat ears via a collection of static images, this paper proposes a counting method based directly on a UAV video multi-objective tracking method and better counting efficiency results. Firstly, we optimized the YOLOv7 model because the basis of the multi-target tracking algorithm is target detection. Simultaneously, the omni-dimensional dynamic convolution (ODConv) design was applied to the network structure to significantly improve the feature-extraction capability of the model, strengthen the interaction between dimensions, and improve the performance of the detection model. Furthermore, the global context network (GCNet) and coordinate attention (CA) mechanisms were adopted in the backbone network to implement the effective utilization of wheat features. Secondly, this study improved the DeepSort multi-objective tracking algorithm by replacing the DeepSort feature extractor with a modified ResNet network structure to achieve a better extraction of wheat-ear-feature information, and the constructed dataset was then trained for the re-identification of wheat ears. Finally, the improved DeepSort algorithm was used to calculate the number of different IDs that appear in the video, and an improved method based on YOLOv7 and DeepSort algorithms was then created to calculate the number of wheat ears in large fields. The results show that the mean average precision (mAP) of the improved YOLOv7 detection model is 2.5% higher than that of the original YOLOv7 model, reaching 96.2%. The multiple-object tracking accuracy (MOTA) of the improved YOLOv7–DeepSort model reached 75.4%. By verifying the number of wheat ears captured by the UAV method, it can be determined that the average value of an L1 loss is 4.2 and the accuracy rate is between 95 and 98%; thus, detection and tracking methods can be effectively performed, and the efficient counting of wheat ears can be achieved according to the ID value in the video.

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A deep learning method for oriented and small wheat spike detection (OSWSDet) in UAV images

Cited by 31 publications

References 31 publications

Improving multi-scale detection layers in the deep learning network for wheat spike detection based on interpretive analysis

Improving multi-scale detection layers in the deep learning network for wheat spike detection based on interpretive analysis

Bridge bottom crack detection and modeling based on faster R‐CNN and BIM

Research on the Method of Counting Wheat Ears via Video Based on Improved YOLOv7 and DeepSort

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