Walnut Ripeness Detection Based on Coupling Information and Lightweight YOLOv4

To realize rapid and accurate ripeness detection for walnut on mobile terminals such as mobile phones, we propose a method based on coupling information and lightweight YOLOv4. First, we collected 50 walnuts at each ripeness (Unripe, Mid-ripe, Ripe, Over-ripe) to determine the kernel oil content. Pearson correlation analysis and one-way analysis of variance (ANOVA) prove that the division of walnut ripeness reflects the change in kernel oil content. It is feasible to estimate the kernel oil content by detecting the ripeness of walnut. Next, we achieve ripeness detection based on lightweight YOLOv4. We adopt MobileNetV3 as the backbone feature extractor and adopt depthwise separable convolution to replace the traditional convolution. We design a parallel convolution structure with depthwise convolution stacking (PCSDCS) to reduce parameters and improve feature extraction ability. To enhance the model’s detection ability for walnuts in the growth-intensive areas, we design a Gaussian Soft DIoU non-maximum suppression (GSDIoU-NMS) algorithm. The dataset used for model optimization contains 3600 images, of which 2880 images in the training set, 320 images in the validation set, and 400 images in the test set. We adopt a multi-training strategy based on dynamic learning rate and transfer learning to get training weights. The lightweight YOLOv4 model achieves 94.05%, 90.72%, 88.30%, 76.92 FPS, and 38.14 MB in mean average precision, precision, recall, average detection speed, and weight capacity, respectively. Compared with the Faster R-CNN model, EfficientDet-D1 model, YOLOv3 model, and YOLOv4 model, the lightweight YOLOv4 model improves 8.77%, 4.84%, 5.43%, and 0.06% in mean average precision, 74.60 FPS, 55.60 FPS, 38.83 FPS, and 46.63 FPS in detection speed, respectively. And the lightweight YOLOv4 is 84.4% smaller than the original YOLOv4 model in terms of weight capacity. This paper provides a theoretical reference for the rapid ripeness detection of walnut and exploration for the model’s lightweight.

Research on Real-Time Face Key Point Detection Algorithm Based on Attention Mechanism

The existing face detection methods were affected by the network model structure used. Most of the face recognition methods had low recognition rate of face key point features due to many parameters and large amount of calculation. In order to improve the recognition accuracy and detection speed of face key points, a real-time face key point detection algorithm based on attention mechanism was proposed in this paper. Due to the multiscale characteristics of face key point features, the deep convolution network model was adopted, the attention module was added to the VGG network structure, the feature enhancement module and feature fusion module were combined to improve the shallow feature representation ability of VGG, and the cascade attention mechanism was used to improve the deep feature representation ability. Experiments showed that the proposed algorithm not only can effectively realize face key point recognition but also has better recognition accuracy and detection speed than other similar methods. This method can provide some theoretical basis and technical support for face detection in complex environment.

Research on Energy Efficiency Management of Forklift Based on Improved YOLOv5 Algorithm

As an important tool for loading, unloading, and distributing palletized goods, forklifts are widely used in different links of industrial production process. However, due to the rapid increase in the types and quantities of goods, item statistics have become a major bottleneck in production. Based on machine vision, the paper proposes a method to count the amount of goods loaded and unloaded within the working time limit to analyze the efficiency of the forklift. The proposed method includes the data preprocessing section and the object detection section. In the data preprocessing section, through operations such as framing and clustering the collected video data and using the improved image hash algorithm to remove similar images, a new dataset of forklift goods was built. In the object detection section, the attention mechanism and the replacement network layer were used to improve the performance of YOLOv5. The experimented results showed that, compared with the original YOLOv5 model, the improved model is lighter in size and faster in detection speed without loss of detection precision, which could also meet the requirements for real-time statistics on the operation efficiency of forklifts.

A Multiperson Pose Estimation Method Using Depthwise Separable Convolutions and Feature Pyramid Network

In the process of multiperson pose estimation, there are problems such as slow detection speed, low detection accuracy of key point targets, and inaccurate positioning of the boundaries of people with serious occlusion. A multiperson pose estimation method using depthwise separable convolutions and feature pyramid network is proposed. Firstly, the YOLOv3 target detection algorithm model based on the depthwise separable convolution is used to improve the running speed of the human body detector. Then, based on the improved feature pyramid network, a multiscale supervision module and a multiscale regression module are added to assist training and to solve the difficult key point detection problem of the human body. Finally, the improved soft-argmax method is used to further eliminate redundant attitudes and improve the accuracy of attitude boundary positioning. Experimental results show that the proposed model has a score of 73.4% in AP on the 2017 COCO test-dev dataset, and it scored 86.24% on [email protected] on the MPII dataset.

A Fast Postprocessing Algorithm for the Overlapping Problem in Wafer Map Detection

Mixed defects have become increasingly popular in defect detection and one of the hottest research areas in wafer maps. Postprocessing methods used to solve the overlapping problem in mass mixed defects have a poor detection speed, which is insufficient for rapid defect detection. In this paper, the fast-soft nonmaximum suppression (fs-NMS) method is proposed to solve this problem. The score of the detection box is updated by optimizing the penalty distribution function. Further, this paper analyzes the performance of the fs-NMS method in wafer defect detection. As a penalty, the logistic function is used, and experiments are conducted using single-stage and two-stage detectors. The final results show that, compared to the soft-NMS, the efficiency for the single-stage and two-stage detectors is increased on average by 9.63% and 21.72%, respectively.

Diseases Detection of Occlusion and Overlapping Tomato Leaves Based on Deep Learning

Background: In view of the existence of light shadow, branches occlusion, and leaves overlapping conditions in the real natural environment, problems such as slow detection speed, low detection accuracy, high missed detection rate, and poor robustness in plant diseases and pests detection technology arise.Results: Based on YOLOv3-tiny network architecture, to reduce layer-by-layer loss of information during network transmission, and to learn from the idea of inverse-residual block, this study proposes a YOLOv3-tiny-IRB algorithm to optimize its feature extraction network, improve the gradient disappearance phenomenon during network deepening, avoid feature information loss, and realize network multilayer feature multiplexing and fusion. The network is trained by the methods of expanding datasets and multiscale strategies to obtain the optimal weight model.Conclusion: The experimental results show that when the method is tested on the self-built tomato diseases and pests dataset, and while ensuring the detection speed (206 frame rate per second), the mean Average precision (mAP) under three conditions: (a) deep separation, (b) debris occlusion, and (c) leaves overlapping are 98.3, 92.1, and 90.2%, respectively. Compared with the current mainstream object detection methods, the proposed method improves the detection accuracy of tomato diseases and pests under conditions of occlusion and overlapping in real natural environment.

Polyp Detection from Colorectum Images by Using Attentive YOLOv5

Background: High-quality colonoscopy is essential to prevent the occurrence of colorectal cancers. The data of colonoscopy are mainly stored in the form of images. Therefore, artificial intelligence-assisted colonoscopy based on medical images is not only a research hotspot, but also one of the effective auxiliary means to improve the detection rate of adenomas. This research has become the focus of medical institutions and scientific research departments and has important clinical and scientific research value. Methods: In this paper, we propose a YOLOv5 model based on a self-attention mechanism for polyp target detection. This method uses the idea of regression, using the entire image as the input of the network and directly returning the target frame of this position in multiple positions of the image. In the feature extraction process, an attention mechanism is added to enhance the contribution of information-rich feature channels and weaken the interference of useless channels; Results: The experimental results show that the method can accurately identify polyp images, especially for the small polyps and the polyps with inconspicuous contrasts, and the detection speed is greatly improved compared with the comparison algorithm. Conclusions: This study will be of great help in reducing the missed diagnosis of clinicians during endoscopy and treatment, and it is also of great significance to the development of clinicians’ clinical work.

Research on Lightweight Citrus Flowering Rate Statistical Model Combined with Anchor Frame Clustering Optimization

At present, learning-based citrus blossom recognition models based on deep learning are highly complicated and have a large number of parameters. In order to estimate citrus flower quantities in natural orchards, this study proposes a lightweight citrus flower recognition model based on improved YOLOv4. In order to compress the backbone network, we utilize MobileNetv3 as a feature extractor, combined with deep separable convolution for further acceleration. The Cutout data enhancement method is also introduced to simulate citrus in nature for data enhancement. The test results show that the improved model has an mAP of 84.84%, 22% smaller than that of YOLOv4, and approximately two times faster. Compared with the Faster R-CNN, the improved citrus flower rate statistical model proposed in this study has the advantages of less memory usage and fast detection speed under the premise of ensuring a certain accuracy. Therefore, our solution can be used as a reference for the edge detection of citrus flowering.

A Vehicle Detection Model Based on 5G-V2X for Smart City Security Perception

Security perception systems based on 5G-V2X have become an indispensable part of smart city construction. However, the detection speed of traditional deep learning models is slow, and the low-latency characteristics of 5G networks cannot be fully utilized. In order to improve the safety perception ability based on 5G-V2X, increase the detection speed in vehicle perception. A vehicle perception model is proposed. First, an adaptive feature extraction method is adopted to enhance the expression of small-scale features and improve the feature extraction ability of small-scale targets. Then, by improving the feature fusion method, the shallow information is fused layer by layer to solve the problem of feature loss. Finally, the attention enhancement method is introduced to increase the center point prediction ability and solve the problem of target occlusion. The experimental results show that the UA-DETRAC data set has a good detection effect. Compared with the vehicle detection capability before the improvement, the detection accuracy and speed have been greatly improved, which effectively improves the security perception capability based on the 5G-V2X system, thereby promoting the construction of smart cities.

Using Channel and Network Layer Pruning Based on Deep Learning for Real-Time Detection of Ginger Images

Consistent ginger shoot orientation helps to ensure consistent ginger emergence and meet shading requirements. YOLO v3 is used to recognize ginger images in response to the current ginger seeder’s difficulty in meeting the above agronomic problems. However, it is not suitable for direct application on edge computing devices due to its high computational cost. To make the network more compact and to address the problems of low detection accuracy and long inference time, this study proposes an improved YOLO v3 model, in which some redundant channels and network layers are pruned to achieve real-time determination of ginger shoots and seeds. The test results showed that the pruned model reduced its model size by 87.2% and improved the detection speed by 85%. Meanwhile, its mean average precision (mAP) reached 98.0% for ginger shoots and seeds, only 0.1% lower than the model before pruning. Moreover, after deploying the model to the Jetson Nano, the test results showed that its mAP was 97.94%, the recognition accuracy could reach 96.7%, and detection speed could reach 20 frames·s−1. The results showed that the proposed method was feasible for real-time and accurate detection of ginger images, providing a solid foundation for automatic and accurate ginger seeding.