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

Traditional machine learning-based pest classification methods are a tedious and time-consuming process A method of multi-class pest detection based on deep learning and convolutional neural networks could be the solution. It automatically extracts the complex features of different pests from the crop pest images. In this paper, various significant deep learning-based object detection models like SSD, EfficientDet, Faster R-CNN, and CenterNet are implemented based on the Tensorflow Object Detection framework. Several significant networks like MobileNet_V2, ResNet101_V1, Inception_ResNet_V2, EfficientNet, and HourGlass104 are employed as backbone networks for these models to extract the different features of the pests. Object detection models are capable of identifying and locating pests in crops. Initially, these models are pre-trained with the COCO dataset and later be fine-tuned to the target pest dataset of 20 different pest classes. After conducting experiments on these models using the pest dataset, we demonstrate that Faster R-CNN_ResNet101_V1 outperformed every other model and achieved mAP of 74.77%. Additionally, it is developed as a lightweight model, whose size is ∼9 MB, and can detect pest objects in 130 milliseconds per image, allowing it to be used on resources-constrained devices commonly used by farmers.

With the rapid development of computer vision and machine vision, methods based on deep learning have achieved good results in the field of object detection, identification, and tracking. However, for the detection and identification of rebars in smart construction sites, it is very difficult to perform accurate real-time detection of rebars by using object detection technology on the equipment in the field because of the problems of the dense cross-section between bundled bars, the mutual adhesion of cross-section boundaries, and mutual occlusion between cross-sections. To address the above problems, we propose a multi-scale rebar detection network RebarNet with an embedded attention mechanism based on YOLOv5, combining the K-means++ algorithm, attention mechanism, a newly designed SD_IoU Loss, and multi-scale feature fusion, aiming to solve the problems of missed and false detection in dense small object detection. Due to the problems of scarce rebar cross-section datasets, no publicly available large datasets, and weak rebar cross-sectional features, we constructed a new rebar cross-sectional dataset, used a semi-automatic annotation method to annotate part of the dataset, and then used the data enhancement algorithm to expand the rebar dataset. The experimental results show that the average accuracy (mAP) of our proposed RebarNet network is 97.9%, which is comparable to mainstream target detection algorithms such as Faster R-CNN, SSD, RetinaNet, CenterNet, CornerNet, YOLOv3, YOLOv4, and YOLOv5s. mAP0.5 is improved by 8.1%, 13%, 26.4%, 25.8%, 26.2%, 11.7%, 7.6%, and 9%, respectively. In addition, the frames per second (FPS) transmission reaches 89 frames per second, the model weight is only 17.0 MB. In summary, the proposed RebarNet can effectively reduce missed and false detections in the rebar counting detection task based on real-time detection.

Abstract— As cities become smarter, the need for real-time threat detection in surveillance systems increases. Firearm-related incidents in public areas are highly critical, where even a small delay can lead to serious consequences. In this study, we propose a deep- learning-based framework for firearm detection using the YOLOv7 model. To improve performance, we integrated a Convolutional Block Attention Module (CBAM) and a Bi-directional Feature Pyramid Network (BiFPN). The model was trained on a diverse dataset of 18,500 images and 6,200 negative samples to reduce false alarms. It achieves high accuracy with a 95.5% mAP@0.5 and operates at 48 FPS. The false-positive rate was reduced to 5.2%, which was significantly lower than the baseline. For real-world use, the system was optimized for NVIDIA Jetson Nano using pruning and INT8 TensorRT, enabling real-time performance at 28 frames per second (FPS). When a firearm is detected, the system sends instant alerts through MQTT to smart city platforms, allowing for a quick response and automation. Overall, this approach combines Deep Learning, Edge AI, and IoT to enable efficient and reliable real-time surveillance, thereby making smart environments safer. Keyword - Firearm Detection, YOLOv7, Deep Learning, Object Detection, Smart City Surveillance, Edge AI, CBAM, BiFPN, Real- Time Monitoring, IoT Security, NVIDIA Jetson Nano, TensorRT Optimization, MQTT Alerts, Computer Vision

As the core technologies represented by Internet of Things (IoT) technology and computer vision technology booming, object detection algorithms based on deep learning have received high attention in various fields. Object detection is not only a core problem in the field of computer vision, but also a prerequisite and basis for many computer vision tasks, and has important applications in the fields of autonomous driving and video surveillance. The task of object detection is to find all the targets of interest in an image and determine their category and location. Object detection includes two tasks: object classification and object localization. However, object classification focuses on the most discriminative part of the feature map, and object localization requires a feature map focusing on the whole region of the object. Therefore, in order to improve the detection accuracy, we propose a CBAM-RetinaNet network model.CBAM (Convolutional Block Attention Module) is a lightweight convolutional attention module, which combines channel and spatial attention mechanism modules. The channel attention module focuses on the meaningful information in the input image, which would lead to an increase in the accuracy of object classification. The spatial attention module focuses on the location information of the object, which may improve the accuracy of object localization. The experiment results show that the proposed achieves better object detection performance on the PASCAL VOC2007 dataset, and the accuracy is 2% higher than that of the RetinaNet model, which has good experimental results.

Accurate and efficient assessment of highland barley (Hordeum vulgare L.) density is crucial for optimizing cultivation and management practices. However, challenges such as overlapping spikes in unmanned aerial vehicle (UAV) images and the computational requirements for high-resolution image analysis hinder real-time detection capabilities. To address these issues, this study proposes an improved lightweight YOLOv5 model for highland barley spike detection. We chose depthwise separable convolution (DSConv) and ghost convolution (GhostConv) for the backbone and neck networks, respectively, to reduce the parameter and computational complexity. In addition, the integration of convolutional block attention module (CBAM) enhances the model’s ability to focus on target object in complex backgrounds. The results show that the improved YOLOv5 model has a significant improvement in detection performance. Precision and recall increased by 3.1% to 92.2% and 86.2%, respectively, with an F1 score of 0.892. The AP0.5 reaches 92.7% and 93.5% for highland barley in the growth and maturation stages, respectively, and the overall mAP0.5 improved to 93.1%. Compared to the baseline YOLOv5n model, the number of parameters and floating-point operations (FLOPs) were reduced by 70.6% and 75.6%, respectively, enabling lightweight deployment without compromising accuracy. In addition,the proposed model outperformed mainstream object detection algorithms such as Faster R-CNN, Mask R-CNN, RetinaNet, YOLOv7, and YOLOv8, in terms of detection accuracy and computational efficiency. Although this study also suffers from limitations such as insufficient generalization under varying lighting conditions and reliance on rectangular annotations, it provides valuable support and reference for the development of real-time highland barley spike detection systems, which can help to improve agricultural management.

Road infrastructure maintenance in developing countries faces unique challenges due to resource constraints and diverse environmental factors. This study addresses the critical need for efficient, accurate, and locally-relevant pavement distress detection methods in these regions. We present a novel deep learning approach combining You Only Look Once (YOLO) object detection models with a Convolutional Block Attention Module (CBAM) to simultaneously detect and classify multiple pavement distress types. The model demonstrates robust performance in detecting and classifying potholes, longitudinal cracks, alligator cracks, and raveling, with confidence scores ranging from 0.46 to 0.93. While some misclassifications occur in complex scenarios, these provide insights into unique challenges of pavement assessment in developing countries. Additionally, we developed a web-based application for real-time distress detection from images and videos. This research advances automated pavement distress detection and provides a tailored solution for developing countries, potentially improving road safety, optimizing maintenance strategies, and contributing to sustainable transportation infrastructure development.

A detection method for dead caged hens based on improved YOLOv7

Strawberry, being an important economic crop, requires a large amount of human labor for harvesting operations. Efficient and non-destructive harvesting by strawberry harvesting robots requires the precise location of the picking points. Current algorithms for locating picking points encounter significant issues with location errors and minimal effective information in complex situations. To improve the accuracy of the location of picking points, this study proposes a visual location method based on composite models. This method employs object detection and instance segmentation models to detect fruits and segment peduncles sequentially, thereby enabling the identification of picking points and inclination on the peduncle. Different object detection algorithms and instance segmentation models were validated to explore the optimal model combination, and the Convolutional Block Attention Module (CBAM) was integrated into YOLOv8s-seg to construct YOLOv8s-seg-CBAM. Test results show that the composite model built with YOLOv8s and YOLOv8s-seg-CBAM achieved a peduncle detection accuracy of 86.2%, with an inference time of 30.6 ms per image. The picking point visual location method based on YOLOv8s and YOLOv8s-seg-CBAM composite models can better balance accuracy and efficiency and can provide more accurate guidance for automated harvesting. © 2024 Society of Chemical Industry.

Efficient and adaptive computer vision systems have been proposed to make computer vision tasks, such as image classification and object detection, optimized for embedded or mobile devices. These solutions, quite recent in their origin, focus on optimizing the model (a deep neural network) or the system by designing an adaptive system with approximation knobs. Despite several recent efforts, we show that existing solutions suffer from two major drawbacks. First , while mobile devices or systems-on-chips usually come with limited resources including battery power, most systems do not consider the energy consumption of the models during inference. Second , they do not consider the interplay between the three metrics of interest in their configurations, namely, latency, accuracy, and energy. In this work, we propose an efficient and adaptive video object detection system— Virtuoso , which is jointly optimized for accuracy, energy efficiency, and latency. Underlying Virtuoso is a multi-branch execution kernel that is capable of running at different operating points in the accuracy-energy-latency axes, and a lightweight runtime scheduler to select the best fit execution branch to satisfy the user requirement. We position this work as a first step in understanding the suitability of various object detection kernels on embedded boards in the accuracy-latency-energy axes, opening the door for further development in solutions customized to embedded systems and for benchmarking such solutions. Virtuoso is able to achieve up to 286 FPS on the NVIDIA Jetson AGX Xavier board, which is up to 45× faster than the baseline EfficientDet D3 and 15× faster than the baseline EfficientDet D0. In addition, we also observe up to 97.2% energy reduction using Virtuoso compared to the baseline YOLO (v3)—a widely used object detector designed for mobiles. To fairly compare with Virtuoso , we benchmark 15 state-of-the-art or widely used protocols, including Faster R-CNN (FRCNN) [NeurIPS’15], YOLO v3 [CVPR’16], SSD [ECCV’16], EfficientDet [CVPR’20], SELSA [ICCV’19], MEGA [CVPR’20], REPP [IROS’20], FastAdapt [EMDL’21], and our in-house adaptive variants of FRCNN+, YOLO+, SSD+, and EfficientDet+ (our variants have enhanced efficiency for mobiles). With this comprehensive benchmark, Virtuoso has shown superiority to all the above protocols, leading the accuracy frontier at every efficiency level on NVIDIA Jetson mobile GPUs. Specifically, Virtuoso has achieved an accuracy of 63.9%, which is more than 10% higher than some of the popular object detection models, FRCNN at 51.1% and YOLO at 49.5%.

With the gradual increase in the annual production of citrus, the efficiency of human labor has become the bottleneck limiting production. To achieve an unmanned citrus picking technology, the detection accuracy, prediction speed, and lightweight deployment of the model are important issues. Traditional object detection methods often fail to achieve balanced effects in all aspects. Therefore, an improved YOLOv7 network model is proposed, which introduces a small object detection layer, lightweight convolution, and a CBAM (Convolutional Block Attention Module) attention mechanism to achieve multi-scale feature extraction and fusion and reduce the number of parameters of the model. The performance of the model was tested on the test set of citrus fruit. The average accuracy (mAP@0.5) reached 97.29%, the average prediction time was 69.38 ms, and the number of parameters and computation costs were reduced by 11.21 M and 28.71 G compared with the original YOLOv7. At the same time, the Citrus-YOLOv7 model's results show that it performs better compared with the current state-of-the-art network models. Therefore, the proposed Citrus-YOLOv7 model can contribute to solving the problem of citrus detection.

Backdoor Attacks with Wavelet Embedding: Revealing and enhancing the insights of vulnerabilities in visual object detection models on transformers within digital twin systems

Leaf counting has numerous applications in plant phenotyping, such as plant growth analysis, yield prediction, and disease detection. However, manual counting is labor-intensive and time-consuming, posing a significant limitation. To address this issue, deep learning-based object detection models are implemented for automated leaf counting. Existing works that explore automated leaf counting are limited due to the inherent structure of plants, e.g., plants are small and occur in overlapping clusters, causing the models to perform sub-optimally. Furthermore, the numerous amount of object detection models introduces a problem of choosing the proper model. Currently, there is limited understanding in determining which object detection architectures perform well for automated leaf counting, particularly when dealing with complex plant anatomy. To address the gap in the literature, three foundational object detection models with different characteristics are compared: YOLOv8, YOLOv12, and Faster R-CNN. The three models chosen display key distinctions such as one-stage vs. two-stage detections and convolution-based vs. attention-based. To provide comprehensive results, multiple optimization techniques were applied to each model. Our experimental results showed that YOLOv8 had the best performance on all performance metrics (mAP50, mAP5095, IOU, MAE, and MAPE). Specifically, YOLOv8 achieved an IOU of 0.6412, while YOLOv12 and Faster R-CNN only achieved an IOU of 0.567 and 0.606, respectively.

Effective agricultural management in maize production operations starts with the early quantification of seedlings. Accurately determining plant presence allows growers to optimize planting density, allocate resources, and detect potential growth issues early on. This study provides a comprehensive analysis of the performance of various object detection models in maize production, with a focus on the effects of planting density, growth stages, and flight altitudes. The findings of this study demonstrate that one-stage models, particularly YOLOv8n and YOLOv5n, demonstrated superior performance with AP50 scores of 0.976 and 0.951, respectively, outperforming two-stage models in terms of resource efficiency and seedling quantification accuracy. YOLOv8n, along with Deformable DETR, Faster R-CNN, and YOLOv3-tiny, were identified for further examination based on their performance metrics and architectural features. The study also highlights the significant impact of plant density and growth stage on detection accuracy. Increased planting density and advanced growth stages (particularly V6) were associated with decreased model accuracy due to increased leaf overlap and image complexity. The V2–V3 growth stages were identified as the optimal periods for detection. Additionally, flight altitude negatively affected image resolution and detection accuracy, with higher altitudes leading to poorer performance. In field applications, YOLOv8n proved highly effective, maintaining robust performance across different agricultural settings and consistently achieving rRMSEs below 1.64% in high-yield fields. The model also demonstrated high reliability, with Recall, Precision, and F1 scores exceeding 99.00%, affirming its suitability for practical agricultural use. These findings suggest that UAV-based image collection systems employing models like YOLOv8n can significantly enhance the accuracy and efficiency of seedling detection in maize production. The research elucidates the critical factors that impact the accuracy of deep learning detection models in the context of corn seedling detection and selects a model suited for this specific task in practical agricultural production. These findings offer valuable insights into the application of object detection technology and lay a foundation for the future development of precision agriculture, particularly in optimizing deep learning models for varying environmental conditions that affect corn seedling detection.

MC-ShuffleNetV2: A lightweight model for maize disease recognition

The detection of objects on water surfaces is a pivotal technology for the perceptual systems of unmanned surface vehicles (USVs). This paper proposes a novel real-time target detection system designed to address the challenges posed by indistinct bottom boundaries and foggy imagery. Our method enhances the YOLOv8s model by incorporating the convolutional block attention module (CBAM) and a self-attention mechanism, examining their impact at various integration points. A dynamic sample assignment strategy was introduced to enhance the precision of our model and accelerate its convergence. To address the challenge of delineating bottom boundaries with clarity, our model employs a two-strategy approach: a threshold filter and a feedforward neural network (FFN) that provides targeted guidance for refining these boundaries. Our model demonstrated exceptional performance, achieving a mean average precision (mAP) of 47.1% on the water surface object dataset, which represents a 1.7% increase over the baseline YOLOv8 model. The dynamic sample assignment strategy contributes a 1.0% improvement on average precision at the intersection over union (IoU) threshold of 0.5 (AP0.5), while the FFN strategy fine-tunes the bottom boundaries and achieves an additional 0.8% improvement in average precision at IoU threshold of 0.75 (AP0.75). Furthermore, ablation studies have validated the versatility of our approach, confirming its potential for integration into various detection frameworks.