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"Edge-Computing Video Analytics Solution for Automated Plastic-Bag Cont" by Umair Iqbal, Johan Barthelemy et al.

The increased global waste generation rates over the last few decades have made the waste management task a significant problem. One of the potential approaches adopted globally is to recycle a significant portion of generated waste. However, the contamination of recyclable waste has been a major problem in this context and causes almost 75% of recyclable waste to be unusable. For sustainable development, efficient management and recycling of waste are of huge importance. To reduce the waste contamination rates, conventionally, a manual bin-tagging approach is adopted; however, this is inefficient and requires huge labor effort. Within household waste contamination, plastic bags have been found to be one of the main contaminants. Towards automating the process of plastic-bag contamination detection, this paper proposes an edge-computing video analytics solution using the latest Artificial Intelligence (AI), Artificial Intelligence of Things (AIoT) and computer vision technologies. The ....

Artificial Intelligence , Only Look Once , Remondis Contamination Dataset , Frames Per Second , Mean Average Precision , False Positives , False Negatives , True Positives , Artificial Intelligence Of Things Aiot , Computer Vision , Deep Learning , Edge Computing , Aste Contamination ,

"Floodborne Objects Type Recognition Using Computer Vision to Mitigate " by Umair Iqbal, Muhammad Zain Bin Riaz et al.

The presence of floodborne objects (i.e., vegetation, urban objects) during floods is considered a very critical factor because of their non-linear complex hydrodynamics and impacts on flooding outcomes (e.g., diversion of flows, damage to structures, downstream scouring, failure of structures). Conventional flood models are unable to incorporate the impact of floodborne objects mainly because of the highly complex hydrodynamics and non-linear nature associated with their kinematics and accumulation. Vegetation (i.e., logs, branches, shrubs, entangled grass) and urban objects (i.e., vehicles, bins, shopping carts, building waste materials) offer significant materialistic, hydrodynamic and characterization differences which impact flooding outcomes differently. Therefore, recognition of the types of floodborne objects is considered a key aspect in the process of assessing their impact on flooding. The identification of floodborne object types is performed manually by the flood managemen ....

Wollongong City Council , Objects Recognition Dataset , Mean Average Precision , Blockage Of Hydraulic Structures , Computer Vision , Loodborne Objects , Object Detection ,

Separating 'Fused' Humans in Computer Vision

A new paper from the Hyundai Motor Group Innovation Center at Singapore offers a method for separating 'fused' humans in computer vision – those cases where the object recognition framework has found a human that is in some way 'too close' to another human (such as 'hugging' actions, or 'standing be ....

Hyundai Motor Group Innovation Center At Singapore , Google Research , Hyundai Motor Group Innovation Center , Stable Diffusion , Occlusion Copy , Occluded Human Instance Segmentation , Simple Copy Paste , Strong Data Augmentation Method , Instance Segmentation , Reality Bite , Large Vocabulary Instance , Occluded Human , Mean Average Precision , Improving Text To Image , Evan Ling ,

Building Smarter Search Products: 3 Steps for Evaluating Search Algorithms

By Jodi Sloan and Selina Li
Over 2 million users visit Shopify’s Help Center every month to find help solving a problem. They come to the Help Center with different motives: learn how to set up their store, find tips on how to market, or get advice on troubleshooting a technical issue. Our search product helps users narrow down what they’re looking for by surfacing what’s most relevant for them. Algorithms empower search products to surface the most suitable results, but how do you know if they’re succeeding at this mission?
Below, we’ll share the three-step framework we built for evaluating new search algorithms. From collecting data using Kafka and annotation, to conducting offline and online A/B tests, we’ll share how we measure the effectiveness of a search algorithm. ....

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