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Object Detection with an IP Camera using Python and CodeProject.AI Server

The first in a two-part series on detecting objects and evil rodents

Australia , Wyze-cam , Advanced-settings , Add-object , Imagedraw-draw , Python , Rtificial-intelligence , Odeproject-ai , Bject-detection ,

"An improved deep learning-based optimal object detection system from i" by Satya Prakash Yadav, Muskan Jindal et al.

Computer vision technology for detecting objects in a complex environment often includes other key technologies, including pattern recognition, artificial intelligence, and digital image processing. It has been shown that Fast Convolutional Neural Networks (CNNs) with You Only Look Once (YOLO) is optimal for differentiating similar objects, constant motion, and low image quality. The proposed study aims to resolve these issues by implementing three different object detection algorithms—You Only Look Once (YOLO), Single Stage Detector (SSD), and Faster Region-Based Convolutional Neural Networks (R-CNN). This paper compares three different deep-learning object detection methods to find the best possible combination of feature and accuracy. The R-CNN object detection techniques are performed better than single-stage detectors like Yolo (You Only Look Once) and Single Shot Detector (SSD) in term of accuracy, recall, precision and loss.

Fast-convolutional-neural-networks-cnns , Convolutional-neural-networks , Fast-convolutional-neural-networks , Only-look-once , Single-stage-detector , Faster-region-based-convolutional-neural-networks , Single-shot-detector , Chess-piece-identification , Aster-region-based-convolutional-neural-networksr-cnn- , Bject-detection , Ingle-stage-detector-ssd-

"Health Monitoring of Old Buildings in Bangladesh: Detection of Cracks " by Rafiul Bari Angan, Md Safaiat Hossain et al.

Numerous buildings in Bangladesh were constructed without following standard building codes. As a result, those are vulnerable to increased earthquake frequency and variable loads. To address this issue, old buildings need to be monitored frequently by non-destructive testing (NDT) to avoid any failures. The identification of cracks and dampness is one of the most important parts of this test. Generally, this detection part is costly and time-consuming to conduct it manually. To resolve this, the current study has been performed for intelligent structural damage identification based on deep learning techniques. A state-of-the-art Convolutional Neural Network (CNN)-based object detection model YOLOv4-tiny has been used to detect cracks and dampness and repair cost analysis of damages. The study outcome suggests that using our proposed deep model, it is possible to detect building cracks with a mean Average Precision (%mAP) of 72.46%. In addition to traditional structural health monitoring, computer vision-based structural health monitoring enables the development of an easily accessible, cost-effective, real-time crack and dampness detection system.

Bangladesh , Convolutional-neural-network , Average-precision , Crack-and-dampness , Eep-learning , Eep-neural-network-dnn- , Bject-detection , Epair-cost-estimation , Olov4-tiny ,

"Visual blockage assessment at culverts using synthetic images to mitig" by Umair Iqbal, Johan Barthelemy et al.

The assessment of visual blockages in cross-drainage hydraulic structures, such as culverts and bridges, is crucial for ensuring their efficient functioning and preventing flash flooding incidents. The extraction of blockage-related information through computer vision algorithms can provide valuable insights into the visual blockage. However, the absence of comprehensive datasets has posed a significant challenge in effectively training computer vision models. In this study, we explore the use of synthetic data, the synthetic images of culvert (SIC) and the visual hydraulics lab dataset (VHD), in combination with a limited real-world dataset, the images of culvert openings and blockage (ICOB), to evaluate the performance of a culvert opening detector. The Faster Region-based Convolutional Neural Network (Faster R-CNN) model with a ResNet50 backbone was used as the culvert opening detector. The impact of synthetic data was evaluated through two experiments. The first involved training the model with different combinations of synthetic and real-world data, while the second involved training the model with reduced real-world images. The results of the first experiment revealed that structured training, where the synthetic images of culvert (SIC) were used for initial training and the ICOB was used for fine-tuning, resulted in slightly improved detection performance. The second experiment showed that the use of synthetic data, in conjunction with a reduced number of real-world images, resulted in significantly improved degradation rates.

Faster-convolutional-neural-network , Faster-region-based-convolutional-neural-network , Computer-vision , Aming-engines , Bject-detection , Ynthetic-data , Isual-blockage-of-hydraulic-structures ,

"Triple critical feature capture network: A triple critical feature cap" by Zhoufeng Liu, Kaihua Wang et al.

Weakly supervised object detection (WSOD) is becoming increasingly important for computer vision tasks, as it alleviates the burden of manual annotation. Most WSOD techniques rely on multiple instance learning (MIL), which tends to localise the discriminative parts of salient objects instead of the whole object. In addition, network training is often supervised using simple image-level annotations, without including object quantities or location information. However, this can lead to ambiguous differentiation of object instances, both in terms of location and semantics. To address these issues, propose an end-to-end triple critical feature capture network (TCFCNet) for WSOD is proposed. Specifically, a multi-task branch, which can perform fully supervised classification and regression task, was integrated with a PCL in an end-to-end network for refining object locations in an online method. A cyclic parametric dropblock module (CPDM) was then designed to help the detector focus on the contextual information by using cyclic masking techniques to maximise the removal of the discriminative components of an object instance to alleviate the part domination problem. Finally, a feature decoupling module (FDM) is proposed to further reduce the ambiguous distinction of object instances by adaptively constructing robust critical features that adapt to multi-task branch for classification and regression tasks, which contains a feature enhancement module and task-specific polarisation functions. Comprehensive experiments are carried out on the challenging Pascal VOC 2007 and VOC 2012 datasets. The proposed method achieves a 54.6% mAP and a 44.3% mAP on the Pascal VOC 2007 and VOC 2012 datasets respectively, showed that our method outperformed existing mainstream techniques by a considerable margin.

Computer-vision , Mage-processing , Bject-detection ,

Innovative MA-X sonar technology receives US patent

MIND Technology recently announced that the United States Patent and Trademark Office (USPTO) has granted a patent for their MA-X technology, which wa...

United-states , Michael-williams , Trademark-office , Klein-blue-technology , United-states-patent , Marine-systems , Sidescan-sonar , Onar , Ydrographic-surveying , Xo , Bject-detection ,

AI In Computer Vision Global Market Report 2023: Emerging

Dublin, March 13, 2023 (GLOBE NEWSWIRE) -- The "AI In Computer Vision Market by Component (Hardware, Software), Function (Training, Inference),...

China , Dublin , Ireland , Asia-pacific , Intel-corporation , Amazon-web-services , Amazon-com-inc , Ibm , Consumer-electronics , Devices-inc-amd , Alphabet-inc , Nvidia-corporation

Neuromorphic Computing Global Market Report 2023: Growing

Dublin, Feb. 15, 2023 (GLOBE NEWSWIRE) -- The "Neuromorphic Computing Market Size, Share & Trends Analysis Report By Application, By End-Use, By...

China , India , Dublin , Ireland , Asia-pacific , Samsung-electronics-co-ltd , Movement-analysis , Intel-corporation , Artificial-intelligence-based-services , Qualcomm-technologies-inc , Deployment-movement-analysis , Brain-corporation

Discovering the WWII Secrets of the Black Sea | Hydro International

The mysterious Black Sea has many secrets yet to be discovered. This research presents the results of the biggest UXO survey project performed on the...

Germany , Black-sea , Oceans-general- , Oceans , Danube-river , Romania-general- , Romania , Bucharest , Bucuresti , Russia , Moskva , Russian

Assert AI secures USD 2 million to accelerate AI product development through computer vision

Mumbai (Maharashtra) [India], November 8 (ANI/PRNewswire): Be it warehousing, agri-storage, logistics & transportation, manufacturing, pharma & healthcare, retail or traffic management, and parking system, most operations remain manually operated even in 2022. Addressing this, Mumbai-based start-up Assert AI has the vision to automate operations, and ensure scalability, security, and actionable business intelligence for businesses of different sizes and types. India's pioneering tech Start-up Assert AI has raised USD 2 Million via Equity funding to expand business in the US and build specific capabilities in the agribusiness domain. The funding will fast-track AI product development to automate detection, security and workflow management through computer vision. The company has raised USD 2 Million via Equity funding to expand business in the US and build specific capabilities in the agribusiness domain. The funding will fast-track AI product development to automate detection, security, and workflow management through computer vision. Assert AI is an award-winning start-up that offers solutions to India's leading ports, manufacturing and logistics companies, banks/ ATMs, and retail chains. Some of their key solutions include face recognition, object detection, weapon detection in high-risk areas, docks utilization, packet counting in warehouses, and safety gear detection, among others. Co-Founder Job Philip explains, "Assert AI has created a niche in various segments by creating state-of-the-art AI solutions. Our solutions are compatible with all different models of CCTV cameras available in the market today. We have both edge and cloud deployment models enabling us to cater to a vast variety of customer needs." Assert AI has not only raised funds through industry veterans, but Arya.ag, India's largest integrated grain commerce platform and a client of Assert AI, has also participated in this round. The agritech start-up already offers its users real-time tracking of their Agri commodity, providing complete assurance on quantity, quality, and payments. Apart from Arya.ag, the AI start-up has also raised funds from Prashant Purker, Ex-MD & CEO of ICICI Venture. The lead equity investor Prashant Purker feels, "Assert AI has developed cutting-edge AI technology that has the potential to solve a multitude of industry problems." Prasanna Rao, Co-Founder & Managing Director, Arya.ag, said, "Assert AI is uniquely placed to solve customer needs around video analytics. In fragmented agri value chains, assurance is essential to bridge the trust gap. With the partnership with Assert AI, Arya.ag intends to strengthen its blockchain offerings and AI & deep tech vision for unprecedented visibility and assurance for its storage, financing, and commerce offerings." Computer Vision, in particular, gives the machines a pair of eyes and the sense of sight through ML algorithms. It is deployed across several areas allowing automatic annotation of images and video data with the help of trained models developed using ML. Deploying computer vision, Assert AI generates data that never existed before, thereby helping organizations look at their operations in entirely different ways. Assert AI is one of the pioneers in India to drive the shift towards automation, security, and efficiency amidst various degrees of complexities through Computer Vision. Started by Job Philip and Nitin Jain, the firm currently employs over fifty people with offices across Bangalore, Mumbai, Pune, Delhi, and Chicago. Know more: assertai.com This story has been provided by PRNewswire. ANI will not be responsible in any way for the content of this article. (ANI/PRNewswire)

Bangalore , Karnataka , India , Delhi , Mumbai , Maharashtra , Chicago , Illinois , United-states , Pune , Prashant-purker , Prasanna-rao