Industrial images are often captured under full-time and full-weather conditions, leading to inevitable noise during the imaging process, which can impact subsequent detection algorithms. In recent years, image denoising with neural networks has been the rapid development. However, training such networks typically requires a large dataset, which is scarce in publicly available industrial image databases. In this paper, we propose a novel approach termed Zero-Shot Industrial Image Lightweight Denoising (ZSILD) network, which effectively denoises single noisy industrial image without the need for datasets. First, we sample the paired neighbour pixels of a random noisy industrial image, which are then utilized to train a lightweight denoising network. Second, we design a lightweight depthwise convolutions network based on bottleneck residual structure with shortcut connections. Finally, this network is trained on the sampled pairs using a novel loss function aimed at enhancing denoising performance. Our experiments conduct on real-world industrial ambient noise demonstrate that our ZSILD method outperforms existing denoising techniques, all while requiring comparatively minimal computational resources.
There is a necessary combustion facility called the flare stack to ensure production safety in every petrochemical plant, smelter, and refinery all over the world. Because of the incomplete combustion of flare gas, the flare stack discharges a large amount of smoke into the atmosphere and make it dirty. The air pollution is becoming more and more serious caused worldwide concern. Hence, there is in desperate need of an efficient and available smoke detection method to protect the atmosphere. To this end, we present a novel Self-Attention Weight Transformer (SAWT) that can detect accurately smoke, then guarantee full combustion of the flare stack. First, we are concerned about the weight relationship between channels and draw on the self-attention mechanism in the MobileViT block structure to adaptively adjust channel-wise feature weight. Second, we leverage short connections to thoroughly learn and fuse local and global features. Results of experiments on a real smoke dataset reveal that the proposed SAWT achieves superior performance to the popular deep CNNs and state-of-the-art smoke detection algorithms.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.