Plant species recognition is a critical step in protecting plant diversity. Leaf-based plant species recognition research is important and challenging due to the large within-class difference and between-class similarity of leaves and the rich inconsistent leaves with different sizes, colors, shapes, textures, and venations. Most existing plant leaf recognition methods typically normalize all leaf images to the same size and then recognize them at one scale, which results in unsatisfactory performances. A novel multiscale convolutional neural network with attention (AMSCNN) model is constructed for plant species recognition. In AMSCNN, multiscale convolution is used to learn the low-frequency and high-frequency features of the input images, and an attention mechanism is utilized to capture rich contextual relationships for better feature extraction and improving network training. Extensive experiments on the plant leaf dataset demonstrate the remarkable performance of AMSCNN compared with the hand-crafted feature-based methods and deep-neural network-based methods. The maximum accuracy attained along with AMSCNN is 95.28%.

/pdf/multiscale-convolutional-neural-networks-with-attention-for-3l04da0n4k.pdf

Multiscale Convolutional Neural Networks with Attention for Plant Species Recognition.

Crop pests seriously affect the yield and quality of crop. To timely and accurately control crop pests is particularly crucial for crop security, quality of life and a stable agricultural economy. Crop pest detection in field is an essential step to control the pests. The existing convolutional neural network (CNN) based pest detection methods are not satisfactory for small pest recognition and detection in field because the pests are various with different colors, shapes and poses. A three-scale CNN with attention (TSCNNA) model is constructed for crop pest detection by adding the channel attention and spatial mechanisms are introduced into CNN. TSCNNA can improve the interest of CNN for pest detection with different sizes under complicated background, and enlarge the receptive field of CNN, so as to improve the accuracy of pest detection. Experiments are carried out on the image set of common crop pests, and the precision is 93.16%, which is 5.1% and 3.7% higher than ICNN and VGG16, respectively. The results show that the proposed method can achieve both high speed and high accuracy of crop pest detection. This proposed method has certain practical significance of real-time crop pest control in the field.

/pdf/crop-pest-detection-by-three-scale-convolutional-neural-3ezc4eua.pdf

Crop pest detection by three-scale convolutional neural network with attention

Facial paralysis is a debilitating condition that weakens or damages facial muscles resulting in asymmetric or abnormal facial movements. To aid in the diagnosis and rehabilitation of facial paralysis, researchers have developed machine learning and deep learning computer-aided diagnosis systems. However, machine learning models have limitations as they rely on facial landmark techniques and manual face palsy region extraction methods to obtain spatial information. Moreover, deep learning models need large, labelled datasets for training whereas existing available facial paralysis datasets are small and restricted. This presents significant challenges, including difficulties in data acquisition, insufficient patient numbers, and inadequate diversity within the datasets. These limitations can potentially restrict the generalizability of these models and introduce biases in the resulting outcomes. In this study, we propose an approach for the diagnosis and grading of facial paralysis comprised of two datasets, one from MEEI (Massachusetts Eye and Ear Infirmary) videos of patients and the other from the YFP (YouTube Face Palsy) dataset. The model uses a transfer learning approach to fine-tune the VGGFace model, which is pre-trained on facial images, on the prepared datasets for facial paralysis. The resultant model was subsequently renamed as FP-VGGFace for the purpose of this research. Additionally, two more pre-trained models on facial images, ResNet50 and VGG16, are also fine-tuned for the facial paralysis task. This was undertaken to conduct a performance comparison of multiple models on the prepared dataset. The findings indicate that the models exhibit high accuracy, benefiting from pre-training on a diverse dataset that enables the capture of spatial information from facial images. The FP-VGGFace model achieves the best accuracy (99.3%) and F1-score (99.3%) surpassing all benchmark models. This study underscores the potential of utilizing pre-trained deep learning models for the diagnosis and rehabilitation of facial paralysis.

A Transfer Learning Approach for Facial Paralysis Severity Detection

Deep Learning Techniques in Leaf Image Segmentation and Leaf Species Classification: A Survey

Abstract The research of plant seeds has always been a focus of agricultural and forestry research, and seed identification is an indispensable part of it. With the continuous application of artificial intelligence technology in the field of agriculture, seed identification through computer vision can effectively promote the development of agricultural and forestry wisdom. Data is the foundation of computer vision, but there is a lack of suitable datasets in the agricultural field. In this paper, a seed dataset named LZUPSD is established. A device based on mobile phones and macro lenses was established to acquire images. The dataset contains 4496 images of 88 different seeds. This dataset can not only be used as data for training deep learning models in the computer field, but also provide important data support for agricultural and forestry research. As an important resource in this field, this dataset plays a positive role in modernizing agriculture and forestry. 

A dataset for fine-grained seed recognition

The identification of plants has great significance in the study of plants, and it has applications in plant classification and medical research. With the development of computer vision, plants identification based on deep learning methods can effectively carry out. At present, most of the existing methods use the traditional features such as the shape and texture features for plant identification, and these methods are applicable to plants with large differences in their shapes. Therefore, we develop a multi-branch convolutional neural network with attention (MCNNA) to extract the effective features, and the first part of MCNNA is an attention block, which is used to reduce the influence of background, and the latter part is multi-branch convolutional neural network, which is used to extract the multi-view features through multi-channel. Experiments have shown that our proposed method has better performances for the classification of plants than the traditional methods. We tested our method on the dataset BJFU100 and obtained final accuracy of 97.89%, and we have the accuracy of 93.35% on our own image dataset.

Plant Identification Based on Multi-branch Convolutional Neural Network with Attention

Facial nerve paralysis adversely affects both mental and physical health of patients. Several degree evaluation methods for facial paralysis have been put forward based on the static facial asymmetry and computer vision technologies. However, traditional methods still suffer from two drawbacks: (1) the facial movement information is ignored which could be important for facial paralysis analysis; (2) the shallow machine learning models used by them may not well extract effective facial features for facial paralysis of different grades. In this paper, we present Triple-stream Long Short Term Memory (LSTM) to evaluate the severity degree of facial paralysis automatically. Hence, the Triple-stream LSTM employs two LSTM sub-networks to extract local movement features from the two involved facial regions and one LSTM sub-network to extract global features from the combination of the two regions. Then, the extracted high level representations from the three LSTMs are fused for final severity degree evaluation. By extracting both local and global movement features from the involved regions in an optimal sense, the final fused features could express the facial features for different degrees of facial nerve paralysis more effectively. The experimental results have verified the effectiveness of Triple-stream LSTM for evaluating the severity of facial paralysis.

Automatic Degree Evaluation of Facial Nerve Paralysis Based on Triple-stream Long Short Term Memory

The topic of object detection has been largely improved recently, especially with the development of convolutional neural network. However, there still exist a lot of challenging cases, such as small object, compact and dense or highly overlapping object. Existing methods can detect multiple objects wonderfully, but because of the slight changes between frames, the detection effect of the model will become unstable, the detection results may result in dropping or increasing the object. In the pedestrian flow detection task, such phenomenon can not accurately calculate the flow. To solve this problem, in this paper, we describe the new function for real-time multi-object detection in sports competition and pedestrians flow detection in public based on deep learning. Our work is to extract a video clip and solve this frame of clips efficiently. More specfically, our algorithm includes two stages: judge method and optimization method. The judge can set a maximum threshold for better results under the model, the threshold value corresponds to the upper limit of the algorithm with better detection results. The optimization method to solve detection jitter problem. Because of the occurrence of frame hopping in the video, and it will result in the generation of video fragments discontinuity. We use optimization algorithm to get the key value, and then the detection result value of index is replaced by key value to stabilize the change of detection result sequence. Based on the proposed algorithm, we adopt wanfang sports competition dataset as the main test dataset and our own test dataset for YOLOv3-Abnormal Number Version(YOLOv3-ANV), which is 5.4% average improvement compared with existing methods. Also, video above the threshold value can be obtained for further analysis. Spontaneously, our work also can used for pedestrians flow detection and pedestrian alarm tasks.

A Video Analysis Method on Wanfang Dataset via Deep Neural Network

The topic of object detection has been largely improved recently, especially with the development of convolutional neural network. However, there still exist a lot of challenging cases, such as small object, compact and dense or highly overlapping object. Existing methods can detect multiple objects wonderfully, but because of the slight changes between frames, the detection effect of the model will become unstable, the detection results may result in dropping or increasing the object. In the pedestrian flow detection task, such phenomenon can not accurately calculate the flow. To solve this problem, in this paper, we describe the new function for real-time multi-object detection in sports competition and pedestrians flow detection in public based on deep learning. Our work is to extract a video clip and solve this frame of clips efficiently. More specifically, our algorithm includes two stages: judge method and optimization method. The judge can set a maximum threshold for better results under the model, the threshold value corresponds to the upper limit of the algorithm with better detection results. The optimization method to solve detection jitter problem. Because of the occurrence of frame hopping in the video, and it will result in the generation of video fragments discontinuity. We use optimization algorithm to get the key value, and then the detection result value of index is replaced by key value to stabilize the change of detection result sequence. Based on the proposed algorithm, we adopt wanfang sports competition dataset as the main test dataset and our own test dataset for YOLOv3-Abnormal Number Version(YOLOv3-ANV), which is 5.4% average improvement compared with existing methods. Also, video above the threshold value can be obtained for further analysis. Spontaneously, our work also can used for pedestrians flow detection and pedestrian alarm tasks. Further more, all the code are publicly available for further research: https://github.com/kangjinlong/yolov3_Abnormal-video.

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Plant Identification Based on Multi-branch Convolutional Neural Network with Attention

Automatic Degree Evaluation of Facial Nerve Paralysis Based on Triple-stream Long Short Term Memory

A Video Analysis Method on Wanfang Dataset via Deep Neural Network

A Video Analysis Method on Wanfang Dataset via Deep Neural Network