In recent years, autonomous unmanned aerial vehicle (UAV) technology has seen rapid advancements, significantly improving operational efficiency and mitigating risks associated with manual tasks in domains such as industrial inspection, agricultural monitoring, and search-and-rescue missions. Despite these developments, existing UAV inspection systems encounter two critical challenges: limited reliability in complex, unstructured, and GNSS-denied environments, and a pronounced dependency on skilled operators. To overcome these limitations, this study presents a LiDAR-based UAV inspection system employing a dual-phase workflow: human-in-the-loop inspection and autonomous inspection. During the human-in-the-loop phase, untrained pilots are supported by autonomous obstacle avoidance, enabling them to generate 3D maps, specify inspection points, and schedule tasks. Inspection points are then optimized using the Traveling Salesman Problem (TSP) to create efficient task sequences. In the autonomous phase, the quadrotor autonomously executes the planned tasks, ensuring safe and efficient data acquisition. Comprehensive field experiments conducted in various environments, including slopes, landslides, agricultural fields, factories, and forests, confirm the system's reliability and flexibility. Results reveal significant enhancements in inspection efficiency, with autonomous operations reducing trajectory length by up to 40\% and flight time by 57\% compared to human-in-the-loop operations. These findings underscore the potential of the proposed system to enhance UAV-based inspections in safety-critical and resource-constrained scenarios.

The industry increasingly relies on deep learning (DL) technology for manufacturing inspections, which are challenging to automate with rule-based machine vision algorithms. DL-powered inspection systems derive defect patterns from labeled images, combining human-like agility with the consistency of a computerized system. However, finite labeled datasets often fail to encompass all natural variations necessitating Continuous Training (CT) to regularly adjust their models with recent data. Effective CT requires fresh labeled samples from the original distribution; otherwise, selfgenerated labels can lead to silent performance degradation. To mitigate this risk, we develop a robust CT-based maintenance approach that updates DL models using reliable data selections through a two-stage filtering process. The initial stage filters out low-confidence predictions, as the model inherently discredits them. The second stage uses variational auto-encoders and histograms to generate image embeddings that capture latent and pixel characteristics, then rejects the inputs of substantially shifted embeddings as drifted data with erroneous overconfidence. Then, a fine-tuning of the original DL model is executed on the filtered inputs while validating on a mixture of recent production and original datasets. This strategy mitigates catastrophic forgetting and ensures the model adapts effectively to new operational conditions. Evaluations on industrial inspection systems for popsicle stick prints and glass bottles using critical real-world datasets showed less than 9% of erroneous self-labeled data are retained after filtering and used for fine-tuning, improving model performance on production data by up to 14% without compromising its results on original validation data.

General Motors is bringing artificial intelligence into the vehicle inspection process. The automaker is making an undisclosed "strategic investment" in Israeli startup UVeye, which makes vehicle diagnostic systems that use sensors and AI to quickly identify damaged parts or maintenance issues. The investment in UVeye was made by GM Ventures, the automaker's venture fund, which also has investments in a variety of other AI-themed startups. As part of the collaboration, GM will sell UVeye's technology to its dealer network to upgrade their vehicle inspection systems. GM will also work with UVeye on a variety of vehicle inspection technology projects involving used car auctions, fleet operations, and automotive dealership sales.

Manufacturers are making strides toward Industry 4.0, a movement to tie a company's factory floor technology with the internet of things, business and operation systems, supply chain and aftermarket technology, and scores of equipment. That includes vision inspection systems, which are increasingly going high-tech with the addition of machine learning, artificial intelligence (AI) algorithms that can be trained to catch small blemishes and disfigurements. The information returned from AI inspection systems is part of the massive operating information that can sense, analyze, and respond to changing company conditions. The resulting data is used to streamline operations and improve efficiency, which lead to massive savings – the premise of Industry 4.0. AI in computer vision is no stranger to manufacturing Industry 4.0 or to a number of other markets, such as biomedical and consumer goods.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Over the past few years, smart manufacturing initiatives such as digital twins and the internet of things (IoT) has caused Industry 4.0 – the trend toward digital transformation in manufacturing and industrial sectors – to explode. However, robots and drones tasked with visually inspecting machines haven't yet seen the same growth. That is set to change in a big way, Bill Ray, vice president and analyst, emerging technologies and trends at Gartner, told VentureBeat. The robots, drones and cameras that inspect machines to perform predictive maintenance and relay analog information to operations staff can now function autonomously.

What started as a simple home repair project ended with multiple trips to the hardware store, cursing in the aisles, and a vow to never buy from a specific manufacturer ever again. A single defective bolt, which had evaded quality inspection and been packaged, shipped, and unfortunately purchased by me. The product packaging, installation instructions, and final functionality were all exemplary. But a single defective bolt, which costs only pennies in the product's bill of materials, was enough to sour me on the whole experience. Manufacturers and brand owners are under tremendous pressure to ensure premium end-to-end product quality, especially as consumers increasingly demand perfection.

Manufacturers of all sizes struggle with the cost of poor product quality, whether that translates into slower production, decreased profits, or unnecessary waste. Even worse, poor quality can do irreversible damage to brand reputation. In the food and beverage market, 20 percent of consumers say they will not purchase from a brand following a product recall. While artificial intelligence (AI) is gaining favor as a solution to quality problems, it brings a number of new, sometimes confusing, terms. As a first step, many manufacturers ask "What is AI?" Machine vision is a mainstay on today's manufacturing floor, thanks to programmers' ability to continuously train inspection systems to make automated decisions.

Deep learning is seeing tremendous adoption in different industries. One specific area where deep learning has shown great potential is Computer Vision. I personally graduated from a computer vision master's program and went immediately to work in the industry. So what follows is my take on different trends that I am seeing in companies that are using deep learning to tackle challenging computer vision problems. So going back to my studies, in the middle of the master's program, I did an internship in a company in Luxembourg that makes large scanners of wood!

First, the specific "SOLOCAP application" was trained with the help of the intelligent APREX Track AI solution. The software includes various object detector, classifier and standard methods that operate at different levels. Networked accordingly, they ultimately deliver the desired result tailored to the customer. Four control levels with several test points guarantee a reliability rate of over 99.99 percent. In the second step, this application was implemented in the production line right after the first assembly run with APREX Track C&M. The latter was specially developed for the diverse image processing requirements in the industrial sector.

A study by McKinsey & Company found that AI-driven quality testing can increase productivity by up to 50% and defect detection rates by up to 90% compared to human inspection. Though machines with automated optical inspection (AOI), powered by machine vision, have replaced most of the manual processes in the modern assembly line, quality control still remains a huge and costly challenge. The European Commission claims that in some industries 50% of production can be abandoned due to defects, and the defect rate can reach up to 90% in complex production environments. The critical limitation with machine learning AOI systems is in disclosing surface defects where even a slight variant (often invisible to the human eye) can hamper the entire production run and render hundreds to thousands of products useless before the defect is discovered. The economic impact can be devastating.