Traditional maize seedling detection mainly relies on manual observation and experience, which is time-consuming and prone to errors. With the rapid development of deep learning and object-detection technology, we propose a lightweight model LW-YOLOv7 to address the above issues. The new model can be deployed on mobile devices with limited memory and real-time detection of maize seedlings in the field. LW-YOLOv7 is based on YOLOv7 but incorporates GhostNet as the backbone network to reduce parameters. The Convolutional Block Attention Module (CBAM) enhances the network’s attention to the target region. In the head of the model, the Path Aggregation Network (PANet) is replaced with a Bi-Directional Feature Pyramid Network (BiFPN) to improve semantic and location information. The SIoU loss function is used during training to enhance bounding box regression speed and detection accuracy. Experimental results reveal that LW-YOLOv7 outperforms YOLOv7 in terms of accuracy and parameter reduction. Compared to other object-detection models like Faster RCNN, YOLOv3, YOLOv4, and YOLOv5l, LW-YOLOv7 demonstrates increased accuracy, reduced parameters, and improved detection speed. The results indicate that LW-YOLOv7 is suitable for real-time object detection of maize seedlings in field environments and provides a practical solution for efficiently counting the number of seedling maize plants.

/pdf/study-on-lightweight-model-of-maize-seedling-object-1hy8vc9r.pdf

Study on Lightweight Model of Maize Seedling Object Detection Based on YOLOv7

Crop counting is a crucial step in crop yield estimation. By counting, crop growth status can be accurately detected and adjusted, improving crop yield and quality. In recent years, with the rapid development of convolutional neural networks, deep learning-based object detection methods have been widely used in crop counting. By summarizing the research related to crop counting, this paper reviews the development status of object detection and crop counting. It then compares deep learning-based object detection counting methods with other counting methods. The paper also introduces public datasets and evaluation metrics commonly used for algorithmic models and provides a more in-depth analysis of the application of object detection in crop counting. Finally, the current problems that need to be solved, such as the lack of datasets, difficulties in small object counting, occlusion in complex environments, and some future directions are summarized. We hope this review will encourage more researchers to use deep-learning object detection methods in agriculture. 

A survey of deep learning-based object detection methods in crop counting

Sugarcane field re-seeding robot is a promising yield-enhancing technology proposed to solve the seedling absences in sugarcane fields. In this study, in combination with developing the sugarcane field re-seeding robot, an improved YOLOv5s model was proposed to detect sugarcane seedlings and predict seed replenishment positions. Firstly, field images of one-month-old sugarcane seedlings were taken at different light conditions as a dataset. Secondly, the Slim-Neck was introduced to replace the Neck network, which can reduce the complexity of the model while maintaining sufficient accuracy. Thirdly, the Efficient Channel Attention (ECA) module was added to the Backbone network to enhance the model's attention on critical feature information of sugarcane seedlings. Fourthly, the SCYLLA-IoU (SIoU) loss function was introduced to speed up the convergence of the proposed model. Lastly, a method for predicting seed replenishment positions was proposed and verified by the field tests. The experimental results showed that the mean average precision (mAP), precision, and recall of the improved YOLOv5s model were 93.1 %, 92.1 %, and 89.9 %, respectively, and the detection speed was 82 frames per second (FPS), which increased the mAP by 1.5 % and the detection speed by 12.3 % compared to the original YOLOv5s model. In addition, compared with Faster R-CNN, SSD, and YOLOv4-tiny models, the improved YOLOv5s model had a higher accuracy, faster detection speed, and less memory consumption. The field test showed that the real-time detection speed of the improved YOLOv5s model was 23 FPS in Nvidia Jetson TX2. The real-time detection precision of sugarcane seedlings was 97.2 %, and the recall was 86.7 %. The mean relative error between the numbers of seed replenishment positions predicted by the robot and that predicted by the human was 18.7 %. Consequently, the improved YOLOv5s model can efficiently and accurately detect sugarcane seedlings and predict seed replenishment positions. This technology provides valuable visual detection support for the sugarcane field re-seeding robot. 

An improved YOLOv5s model for effectively predict sugarcane seed replenishment positions verified by a field re-seeding robot

Current methodologies for insulator defect detection are hindered by limitations in real-world applicability, spatial constraints, high computational demand, and segmentation challenges. Addressing these shortcomings, this paper presents a robust fast detection algorithm combined segmentation head networks with harnessing self-attention and transformer (HST-Net), which is based on the You Only Look Once (YOLO) v5 to recognize and assess the extent and types of damage on the insulator surface. Firstly, the original backbone network is replaced by the transformer cross-stage partial (Transformer-CSP) networks to enrich the network’s ability by capturing information across different depths of network feature maps. Secondly, an insulator defect segmentation head network is presented to handle the segmentation of defect areas such as insulator losses and flashovers. It facilitates instance-level mask prediction for each insulator object, significantly reducing the influence of intricate backgrounds. Finally, comparative experiment results show that the positioning accuracy and defect segmentation accuracy of the proposed both surpass that of other popular models. It can be concluded that the proposed model not only satisfies the requirements for balance between accuracy and speed in power facility inspection, but also provides fresh perspectives for research in other defect detection domains. 

Segmentation Head Networks with Harnessing Self-Attention and Transformer for Insulator Surface Defect Detection

People are gradually coming around to the idea of living a low-carbon lifestyle and using green transportation, and given the severe urban traffic congestion, electric bicycle commuting has taken over as the preferred mode of short-distance transportation for many. Since batteries are used to power electric bicycles, there are no greenhouse gas emissions while they are in use, which is more in line with the requirement for sustainable development around the world. The public has been increasingly concerned about the safety issues brought on by electric bicycles as a result of the industry’s quick development and the rapid increase in the number of electric bicycles worldwide. The unsafe operation of the elevator and the safety of the building have been seriously compromised by the unauthorized admission of electric bicycles into the elevator. To meet the need for fast detection and identification of electric bicycles in elevators, we designed a modified YOLOv5-based identification approach in this study. We propose the use of the EIoU loss function to address the occlusion problem in electric bicycle recognition. By considering the interaction ratio and overlap loss of the target frames, we are able to enhance localization accuracy and reduce the missed detection rate of occluded targets. Additionally, we introduce the CBAM attention mechanism in both the backbone and head of YOLOv5 to improve the expressive power of feature maps. This allows the model to prioritize important regions of the target object, leading to improved detection accuracy. Furthermore, we utilize the CARAFE operator during upsampling instead of the nearest operator in the original model. This enables our model to recover details and side information more accurately, resulting in finer sampling results. The experimental results demonstrate that our improved model achieves an mAP of 86.35 percent, a recall of 81.8 percent, and an accuracy of 88.0 percent. When compared to the original model under the same conditions, our improved YOLOv5 model shows an average detection accuracy increase of 3.49 percent, a recall increase of 5.6 percent, and an accuracy increase of 3.5 percent. Tests in application scenarios demonstrate that after putting the model on the hardware platform Jeston TX2 NX, stable and effective identification of electric bicycles can be accomplished.

Research on the Rapid Recognition Method of Electric Bicycles in Elevators Based on Machine Vision

Sugarcane seedling emergence is important for sugar production. Manual counting is time-consuming and hardly practicable for large-scale field planting. Unmanned aerial vehicles (UAVs) with fast acquisition speed and wide coverage are becoming increasingly popular in precision agriculture. We provide a method based on improved Faster RCNN for automatically detecting and counting sugarcane seedlings using aerial photography. The Sugarcane-Detector (SGN-D) uses ResNet 50 for feature extraction to produce high-resolution feature expressions and provides an attention method (SN-block) to focus the network on learning seedling feature channels. FPN aggregates multi-level features to tackle multi-scale problems, while optimizing anchor boxes for sugarcane size and quantity. To evaluate the efficacy and viability of the proposed technology, 238 images of sugarcane seedlings were taken from the air with an unmanned aerial vehicle. Outcoming with an average accuracy of 93.67%, our proposed method outperforms other commonly used detection models, including the original Faster R-CNN, SSD, and YOLO. In order to eliminate the error caused by repeated counting, we further propose a seedlings de-duplication algorithm. The highest counting accuracy reached 96.83%, whilst the mean absolute error (MAE) reached 4.6 when intersection of union (IoU) was 0.15. In addition, a software system was developed for the automatic identification and counting of cane seedlings. This work can provide accurate seedling data, thus can support farmers making proper cultivation management decision.

/pdf/identification-and-counting-of-sugarcane-seedlings-in-the-1dprqrp8.pdf

Identification and Counting of Sugarcane Seedlings in the Field Using Improved Faster R-CNN

High-speed railway catenaries are vital components in railway traction power supply systems. To ensure stable contact between the pantograph and the catenary, droppers are positioned between the messenger wire and contact line. The failure of one or more droppers will affect the power supply of the catenary and the operation of the railway. In this paper, we modify the You Only Look Once version five (YOLOv5) model in several ways and propose a method for improving the identification of dropper status and the detection of small defects. Firstly, to focus on small target features, the selective kernel attention module is added to the backbone. Secondly, the feature graphs of different scales extracted from the backbone network are fed into the bidirectional feature pyramid network for multiscale feature fusion. Thirdly, the YOLO head is replaced by a decoupled head to improve the convergence speed and detection accuracy of the model. The experimental results show that the proposed model achieves a mean average precision of 92.9% on the dropper dataset, an increase of 3.8% over the results using YOLOv5s. The detection accuracy of small dropper defects reaches 79.2%, representing an increase of 10.8% compared with YOLOv5s and demonstrating that our model is better at detecting small defects.

/pdf/yolov5s-d-a-railway-catenary-dropper-state-identification-3mkz9xck.pdf

YOLOv5s-D: A Railway Catenary Dropper State Identification and Small Defect Detection Model

Dragon fruit (Hylocereus undatus) is a tropical and subtropical fruit that undergoes multiple ripening cycles throughout the year. Accurate monitoring of the flower and fruit quantities at various stages is crucial for growers to estimate yields, plan orders, and implement effective management strategies. However, traditional manual counting methods are labor-intensive and inefficient. Deep learning techniques have proven effective for object recognition tasks but limited research has been conducted on dragon fruit due to its unique stem morphology and the coexistence of flowers and fruits. Additionally, the challenge lies in developing a lightweight recognition and tracking model that can be seamlessly integrated into mobile platforms, enabling on-site quantity counting. In this study, a video stream inspection method was proposed to classify and count dragon fruit flowers, immature fruits (green fruits), and mature fruits (red fruits) in a dragon fruit plantation. The approach involves three key steps: (1) utilizing the YOLOv5 network for the identification of different dragon fruit categories, (2) employing the improved ByteTrack object tracking algorithm to assign unique IDs to each target and track their movement, and (3) defining a region of interest area for precise classification and counting of dragon fruit across categories. Experimental results demonstrate recognition accuracies of 94.1%, 94.8%, and 96.1% for dragon fruit flowers, green fruits, and red fruits, respectively, with an overall average recognition accuracy of 95.0%. Furthermore, the counting accuracy for each category is measured at 97.68%, 93.97%, and 91.89%, respectively. The proposed method achieves a counting speed of 56 frames per second on a 1080ti GPU. The findings establish the efficacy and practicality of this method for accurate counting of dragon fruit or other fruit varieties.

Fast Recognition and Counting Method of Dragon Fruit Flowers and Fruits Based on Video Stream

: Sugarcane seedling emergence is important for sugar production. Manual counting is time-consuming and hardly practicable for large-scale ﬁeld planting. Unmanned aerial vehicles (UAVs) with fast acquisition speed and wide coverage are becoming increasingly popular in precision agriculture. We provide a method based on improved Faster RCNN for automatically detecting and counting sugarcane seedlings using aerial photography. The Sugarcane-Detector (SGN-D) uses ResNet 50 for feature extraction to produce high-resolution feature expressions and provides an attention method (SN-block) to focus the network on learning seedling feature channels. FPN aggregates multi-level features to tackle multi-scale problems, while optimizing anchor boxes for sugarcane size and quantity. To evaluate the efﬁcacy and viability of the proposed technology, 238 images of sugarcane seedlings were taken from the air with an unmanned aerial vehicle. Outcoming with an average accuracy of 93.67%, our proposed method outperforms other commonly used detection models, including the original Faster R-CNN, SSD, and YOLO. In order to eliminate the error caused by repeated counting, we further propose a seedlings de-duplication algorithm. The highest counting accuracy reached 96.83%, whilst the mean absolute error (MAE) reached 4.6 when intersection of union (IoU) was 0.15. In addition, a software system was developed for the automatic identiﬁcation and counting of cane seedlings. This work can provide accurate seedling data, thus can support farmers making proper cultivation management decision.

Identiﬁcation and Counting of Sugarcane Seedlings in the Field Using Improved Faster R-CNN

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Papers

Identification and Counting of Sugarcane Seedlings in the Field Using Improved Faster R-CNN

YOLOv5s-D: A Railway Catenary Dropper State Identification and Small Defect Detection Model

Fast Recognition and Counting Method of Dragon Fruit Flowers and Fruits Based on Video Stream

Identiﬁcation and Counting of Sugarcane Seedlings in the Field Using Improved Faster R-CNN