Intelligent Detection Using Convolutional Neural Network (ID-CNN)

In this paper, we demonstrate a machine learning algorithm with multiple GPU processing in hematocytes detection. Object detection in compound microscopy images presents a specific task. Microscopy image import directly to a specimen slide under the compound microscope by an image sensor device. We propose Faster R-CNN with a customizing model applying cutting-edge object detection systems. The MicrosisDCN (Microbes Diagnosis with Deep Convolutional Neural Network) deploys Faster R-CNN in python script configures to access multiple GPUs computation with 7168 CUDA cores of dual GPUs with Linux command options: worker_replicas and num_clones equal to several graphics processing units. The datasets consist of hematocytes extracted from raw slides under a microscope. These images separate the cells of interest into three groups: red blood cell (RBCs), white blood cell (WBCs), and platelets. The training dataset consists of 80 percent of the 40,000 images. And the testing dataset consists of 20 percent of the 40,000 images. Our algorithm also provides the result of mean average precision (mAP) and enables multiple GPU training models in Tensorflow and OpenCV. The mAP is the average of average precision (AP) with intersection-over-union (IoU) in measuring the score of object detection accuracy. If the mAP score approaches 1.0, it indicates well accuracy. Our 9,000 steps valuation algorithm model by the python script estimates the mAP in 3 groups: RBCs, WBCs, and platelets as about 0.9147, 0.9664, and 0.9548. The optimum would be at nearly 12,000 steps because the algorithm estimates the mAP model as 0.96 in all types. The experiment aims to verify the neural network model using a compound microscope.

Deep learning target detection based on faster regions with convolutional neural network (Faster R-CNN) features has been applied in image processing successfully, however, it is rarely introduced to the field of hyperspectral image (HSI) target detection due to the tensor characteristics and the lack of training samples of HSI data. In this paper, the target detection based on Faster R-CNN is proposed to HSI with data set adjustment and parameter turning. As a typical tensor data, HSIs contain two-dimensional (2-D) spatial information and one dimensional (1-D) spectral information. It contains more information than ordinary images, and has unique advantages in the field of ground object and sea target detection. Therefore, the original HSI is firstly adjusted to the data set format required by the model, and the final Faster R-CNN sample data set can be achieved by combining the data set of Google Earth images. Next, a Faster R-CNN network suitable for HSI data could be built. Finally, to improve the accuracy of target detection, some parameters of Faster R-CNN would be tuned. The numerical results show that the method has the potential advantages of high precision and high speed in HSI target detection, and will have broad application prospects.

Deep learning has demonstrated significant capabilities for learning image features and presents many opportunities for agricultural automation. Deep neural networks typically require large and diverse training datasets to learn generalizable models. However, this requirement is challenging for applications in agricultural automation systems, since collecting and annotating large amount of training samples from filed crops and greenhouses is an expensive and complicated process due to the large diversity of crops, growth seasons and climate changes. This research proposed a new method for augmenting training dataset using synthesized images that preserves the background context and texture of the data object. A synthetic dataset of 1800 images was generated using a reference dataset and applying image processing techniques. As reference dataset 100 and for evaluating detection performance 230 real images of strawberry flowers were collected in greenhouses. Experimental results demonstrated that the suggested method provides improved performance when applied to the state-of-the-arts convolutional object detectors including Faster R-CNN, SSD, YOLOv3 and CenterNet for the task of strawberry flower detection in non-structured environment. The YOLOv3 w/darknet53 model achieved 46.84% boost in performance with average precision (AP) improved from 39.20% to 86.04% when applied augmentation using synthetic dataset. The AP of Faster R-CNN w/resnet50, SSD w/resnet50 and FPN and CenterNet w/hourglass52 models improved by 15.71, 18.42 and 22.24%, respectively. The Faster R-CNN w/resnet50 model provided most significant strawberry flower detection performance with AP 90.84%, which is higher than SSD w/resnet50 and FPN, YOLOv3 w/darknet53 and CenterNet w/hourglass52 models (88.56%, 86.04 % and 83.82%, respectively). Keywords: Flower detection, deep convolutional neural network, data augmentation, synthetic dataset.

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.

The identification and categorization of parasitic eggs are crucial for diagnosing parasitic infections, which pose a significant threat to global health. These methods also require specialized knowledge and can be expensive. In contrast, recent progress in artificial intelligence, particularly deep learning, has revolutionized this field by automating detection processes with high levels of accuracy and precision, reducing the need for specialized knowledge, and improving diagnostic speed. Transfer learning methods using convolutional neural networks (CNNs)-based models like AlexNet, ResNet, VGG16 and EfficientNet-B4 have yielded promising outcomes. Likewise, cutting-edge object detection models, such as the YOLO series (YOLOv5, YOLOv7, YOLOv8), Faster R-CNN, and TOOD have significantly enhanced detection efficiency. Moreover, innovative architectures like YAC-Net, which incorporate algorithmic modifications, have shown superior performance compared to traditional models like YOLO in addressing domain-specific challenges. Advanced models such as Vision Transformers, Cascade Mask R-CNN, Swin Transformers, and the DETR framework have demonstrated remarkable potential in object detection and classification tasks. DenseNet121, with its efficient feature extraction capabilities, and CoAtNet, a hybrid model leveraging the strengths of ConvNets, Vision Transformers and Ensemble learning have further enriched the field. This study examines these advancements, assesses their performance across key metrics, and explores their applicability in real-world clinical environments, offering insights into current limitations and future directions for improving parasitic egg detection and classification.

Convolution operation in deep convolutional neural networks is the most computationally expensive as compared to other operations. Most of the model computation (FLOPS) in the deep architecture belong to convolution operation. In this paper, we are proposing a novel skip convolution operation that employs significantly fewer computation as compared to the traditional one without sacrificing model accuracy. Skip convolution operation produces structured sparsity in the output feature maps without requiring sparsity in the model parameters for computation reduction. The existing convolution operation performs the redundant computation for object feature representation while the proposed convolution skips redundant computation. Our empirical evaluation for various deep models (VGG, ResNet, MobileNet, and Faster R-CNN) over various benchmarked datasets (CIFAR-10, CIFAR-100, ImageNet, and MS-COCO) show that skip convolution reduces the computation significantly while preserving feature representational capacity. The proposed approach is model-agnostic and can be applied over any architecture. The proposed approach does not require a pretrained model and does train from scratch. Hence we achieve significant computation reduction at training and test time. We are also able to reduce computation in an already compact model such as MobileNet using skip convolution. We also show empirically that the proposed convolution works well for other tasks such as object detection. Therefore, SkipConv can be a widely usable and efficient way of reducing computation in deep CNN models.

Object detection has become the cornerstone of various real-world applications, ranging from autonomous driving where it helps detect road objects to medical imaging for early disease prediction and gesture recognition systems. Due to their ability to operate continuously and cost-effectively, computer vision models are increasingly being employed in surveillance and monitoring tasks. Unlike humans, who can intuitively recognize and understand objects within images, machines require advanced algorithms to mimic such capabilities. A key challenge in computer vision lies in recognizing and tracking objects in real-time with accuracy and reliability. Human visual systems perform these tasks naturally and swiftly, enabling complex activities like driving. For computers to match such performance, efficient object detection models along with additional hardware such as sensors are essential. This study presents a comparative analysis of two popular deep learning-based object detection algorithms You Only Look Once (YOLO) and Faster Region-based Convolution Neural Network (Faster RCNN). Both models are evaluated based on their detection accuracy, speed, and performance in real-time scenarios. The paper aims to highlight the strengths and limitations of each model, offering insights into their suitability for different applications. The findings suggest that while both algorithms have their merits, the choice between them depends on the specific requirements of a task, such as the trade-off between detection speed and precision. Comparative study shows that Faster RCNN outperforms its accuracy compared with YOLO.

Artificial intelligence (AI) is an important tool for improving agricultural tasks. In particular, object detection methods based on convolutional neural networks (CNNs) enable the detection and classification of objects directly in the field. Combined with unmanned aerial vehicles (UAVs, drones), these methods allow efficient crop monitoring. The primary challenge is to develop models that are both accurate and feasible under real-world conditions. This study addresses this challenge by evaluating marigold flower detection using three groups of CNN detectors: canonical models, including YOLOv2, Faster R-CNN, and SSD with their original backbones; modified versions of these detectors using DarkNet-53; and modern architectures, including YOLOv11, YOLOv12, and the RT-DETR. The dataset consisted of 392 images from marigold fields, which were manually labeled and augmented to a total of 940 images. The results showed that YOLOv2 with DarkNet-53 achieved the best performance, with 98.8% mean average precision (mAP) and 97.9% F1-score (F1). SSD and Faster R-CNN also improved, reaching 63.1% and 52.8%, respectively. Modern models obtained strong results: YOLOv11 and YOLOv12 reached 96–97%, and RT-DETR 93.5%. The modification of YOLOv2 allowed this classical detector to compete directly with, and even surpass, recent models. Precision–recall (PR) curves, F1-scores, and complexity analysis confirmed the trade-offs between accuracy and efficiency. These findings demonstrate that while modern detectors are efficient baselines, classical models with updated backbones can still deliver state-of-the-art results for UAV-based crop monitoring.

Detection of metastasis of mediastinal lymph nodes in lung cancer patients with an artificial intelligence model.

Computer vision is developing really fast in recent years, and object detection is now one of the hottest topics. faster region-based convolutional neural network (faster R-CNN) and You Only Look Once (YOLOv3) are two popular modules to solve object detection problems, but they are implemented in different ways and thus have different performance in practice. This article is going to introduce these two modern modules and make a few experiments to compare the performance of each module with various datasets. This analysis will focus on speed, accuracy and the performances in different situations. Two specific studies will be mentioned as two typical examples of object detection application: face mask detection and greenhouse detection from satellite images. At last, the article will draw out a conclusion, make some suggestions for the choice of faster R-CNN and YOLOv3 and make a prospect for the future. It turns out that generally faster R-CNN fits tasks that require high accuracy better and YOLOv3 can realize real-time detection tasks.

Brace sleeve screws are an important component of catenary support devices in high-speed railways. According to the statistical data, there is a much risk for failure of brace sleeve screws during the operation of the catenary system. In order to obtain better results on the fault diagnosis for brace sleeve screws, it is of vital importance to develop a high-precision detection approach for brace sleeve screws. With the aim of solving the problems that the size of the brace sleeve screw is too small and the employed previous methods are difficult to detect accurately, a novel object detection network based on an improved Faster RCNN is proposed. Through the analysis of the factors that influence the detection accuracy for brace sleeve screws and the characteristics of original Faster RCNN, the concepts of discrimination maps and proposal maps are introduced for the improved Faster RCNN. In order to retain the characteristic information of brace sleeve screws, the high-resolution discrimination maps are adopted to identify the object type and the low-resolution proposal maps are adopted to generate the candidate boxes of region with an appropriate anchor scale configuration. Experimental results show that the proposed method has a higher detection accuracy for brace sleeve screws. In addition, compared with original Faster RCNN implementation based on the VGG-16 and ResNet-101 backbone, the detection accuracy of the improved Faster RCNN is increased by 8%. It also provides a new idea for general object detection of small targets.

Object detection has been a key task in computer vision with deep convolutional neural networks being a significant performer. We propose a method named Region Average Pooling that leverages object co-occurrence to improve object detection performance. Given regions of interest in an image, our method augments object detection networks with pooled contextual features from other regions of interest in the scene. We implement our scheme and evaluate it on the Pascal Visual Object Classes (VOC) 2007 and Microsoft Common Objects in Context (MS COCO) datasets. When used as part of the Faster R-CNN object detection framework with VGG-16, we show an increase in mAP from 24.2% to 25.5% over baseline Faster R-CNN and Global Average Pooling when testing on MS COCO.

More than 80% of video surveillance systems are used for monitoring people. Old human detection algorithms, based on background and foreground modelling, could not even deal with a group of people, to say nothing of a crowd. Recent robust and highly effective pedestrian detection algorithms are a new milestone of video surveillance systems. Based on modern approaches in deep learning, these algorithms produce very discriminative features that can be used for getting robust inference in real visual scenes. They deal with such tasks as distinguishing different persons in a group, overcome problem with sufficient enclosures of human bodies by the foreground, detect various poses of people. In our work we use a new approach which enables to combine detection and classification tasks into one challenge using convolution neural networks. As a start point we choose YOLO CNN, whose authors propose a very efficient way of combining mentioned above tasks by learning a single neural network. This approach showed competitive results with state-of-the-art models such as FAST R-CNN, significantly overcoming them in speed, which allows us to apply it in real time video surveillance and other video monitoring systems. Despite all advantages it suffers from some known drawbacks, related to the fully-connected layers that obstruct applying the CNN to images with different resolution. Also it limits the ability to distinguish small close human figures in groups which is crucial for our tasks since we work with rather low quality images which often include dense small groups of people. In this work we gradually change network architecture to overcome mentioned above problems, train it on a complex pedestrian dataset and finally get the CNN detecting small pedestrians in real scenes.

Unmanned Aerial Vehicles (UAVs) have gained significant traction for real-time surveillance applications such as object tracking and search-and-rescue missions. A key enabler of these capabilities is the integration of a real-time, highly accurate object detection system. While available two-stage detectors like R-CNN, Fast R-CNN, Faster R-CNN, and Mask R-CNN achieve high detection accuracy and precise localization by dividing the image into regions and classifying each region, they are often slow, complex, and resource-intensive, making them unsuitable for real-time applications. To address this limitation, this research utilizes a Darknet-based YOLOv3 (You Only Look Once, Version 3), a state-of-the-art algorithm for real-time object detection in videos, live feeds, and images. Leveraging a deep Convolutional Neural Network (CNN), YOLOv3 efficiently learns features and predicts object locations and class probabilities in a single pass, ensuring high-speed and reliable detection. A real-time aerial surveillance system for quadcopter application using machine vision is proposed. The training dataset, obtained from the internet and self-taken images from a camera, was manually annotated into three critical categories: suspect, bandit, and weapon. The dataset was subsequently divided into training and testing subsets. Experimental results demonstrate that the proposed system achieves outstanding detection accuracy, with an overall mean average precision (mAP@0.5) of 93.82%, precision of 94%, and recall of 83%. Compared to a ResNet-50-based Faster R-CNN model, the YOLOv3-based approach outperformed, achieving success rates of 0.81 for the Bandit class and 1.00 for both Suspect and Weapon classes. This research improves drone-based AI for real-time object detection in security systems, enhancing efficiency and adaptability. It also creates specialized datasets, including a tailored bandit dataset, to support future UAV security research.

Basic tasks in computer vision, object detection and localization have many applications in autonomous driving, robotics, surveillance, and healthcare [1]. Deep learning has enabled previously unheard-of levels of accuracy and robustness in object detection and localization tasks, revolutionizing the area in recent years. This paper provides a thorough overview of the most recent developments and approaches in the application of deep learning algorithms for localization and object detection. First, we go over the basic ideas behind object identification and localization, emphasizing the difficulties caused by different item sizes, occlusions, and cluttered backgrounds. We then explore the nuances of deep learning architectures designed specifically for these kinds of tasks: anchor-free approaches like Center Net, single-shot detectors like YOLO, and region-based detectors like Faster R-CNN. We investigate feature extraction networks, object detecting heads, and training procedures for best performance as we investigate the underlying mechanics of these architectures. We also go over important assessment measures and datasets that are necessary for benchmarking and training object detection algorithms. We stress the significance of using strong evaluation techniques to guarantee the dependability and capacity for generalization of the suggested algorithms. Apart from technical concerns, we investigate practical uses cases where object detection and localization are essential, like autonomous vehicle pedestrian identification, surveillance system object tracking, and medical imaging anomaly detection. Lastly, we examine new developments and directions for the discipline, such as integrating multimodal data, handling domain shifts, and improving object detection algorithms' interpretability and fairness. Our goal is to stimulate further developments in computer vision systems by offering scholars, professionals, and hobbyists insightful knowledge about the problems and cutting-edge methods of object recognition and localization through this thorough review. The content of the paper is succinctly summarized in this abstract, which also highlights the significance of deep learning, object detection and localization, important methodology, datasets, applications, and future developments in the field.