This paper presents an algorithm for infrared and visible image fusion using significance detection and Convolutional Neural Networks with the aim of integrating discriminatory features and improving the overall quality of visual perception. Firstly, a global contrast-based significance detection algorithm is applied to the infrared image, so that salient features can be extracted, highlighting high brightness values and suppressing low brightness values and image noise. Secondly, a special loss function is designed for infrared images to guide the extraction and reconstruction of features in the network, based on the principle of salience detection, while the more mainstream gradient loss is used as the loss function for visible images in the network. Afterwards, a modified residual network is applied to complete the extraction of features and image reconstruction. Extensive qualitative and quantitative experiments have shown that fused images are sharper and contain more information about the scene, and the fused results look more like high-quality visible images. The generalization experiments also demonstrate that the proposed model has the ability to generalize well, independent of the limitations of the sensor. Overall, the algorithm proposed in this paper performs better compared to other state-of-the-art methods.

In obtaining color constancy, estimating the illumination of a scene is the most important task. However, due to unknown light sources and the influence of the external imaging environment, the estimated illumination is prone to color ambiguity. In this article, a learning-based multi-scale region-weighed network guided by semantic features is proposed to estimate the illuminated color of the light source in a scene. Cued by the human brain's processing of color constancy, we use image semantics and scale information to guide the process of illumination estimation. First, we put the image and its semantics into the network, and then obtain the region weights of the image at different scales. After that, through a special weight-pooling layer (WPL), the illumination on each scale is estimated. The final illumination is calculated by weighting each scale. The results of extensive experiments on Color Checker and NUS 8-Camera datasets show that the proposed approach is superior to the current state-of-the-art methods in both efficiency and effectiveness.

Color constancy methods are generally based on a simplifying assumption that the spectral distribution of a light source is uniform across scenes. However, in reality, this assumption is often violated because of the presence of multiple light sources, that is, more than two illuminations. In this paper, we propose a unique cascade network of deep multi-scale supervision and single-scale estimation (CN-DMS4) to estimate multi-illumination. The network parameters are supervised and learned from coarse to fine in the training process and estimate only the final thinnest level illumination map in the illumination estimation process. Furthermore, to reduce the influence of the color channel on the Euclidean distance or the pixel-level angle error, a new loss function with a channel penalty term is designed to optimize the network parameters. Extensive experiments are conducted on single and multi-illumination benchmark datasets. In comparison with previous multi-illumination estimation methods, our proposed method displays a partial improvement in terms of quantitative data and visual effect, which provides the future research direction in end-to-end multi-illumination estimation.

This paper presents a novel method to improve drill pressure measurement accuracy in slim-hole drilling within the petroleum industry, a sector often plagued by extreme conditions that compromise data integrity. We introduce a temperature compensation model based on a Chaotic-Initiated Adaptive Whale Optimization Algorithm (C-I-WOA) for optimizing Convolutional Neural Networks (CNNs), dubbed the C-I-WOA-CNN model. This approach enhances the Whale Optimization Algorithm (WOA) initialization through chaotic mapping, boosts the population diversity, and features an adaptive weight recalibration mechanism for an improved global search and local optimization. Our results reveal that the C-I-WOA-CNN model significantly outperforms traditional CNNs in its convergence speed, global searching, and local exploitation capabilities, reducing the average absolute percentage error in pressure parameter predictions from 1.9089% to 0.86504%, thereby providing a dependable solution for correcting temperature-induced measurement errors in downhole settings.

This study presents an innovative method to optimize convolutional neural networks (CNNs) tailored for the prevalent low-power platforms in IoT and embedded devices, which are integral to smart city infrastructures. Our approach introduces a novel “Rotating Convolutional Kernel” technique, designed to significantly reduce the computational load of CNNs while maintaining high accuracy, an essential feature for the constrained processing capabilities of devices produced by Hisilicon, MediaTek, and Novatek, among others. By leveraging the intelligent video engine (IVE) capabilities inherent in low-end CPUs, our optimized YOLOv3-Tiny model, with fewer than 100 K parameters and specifically fine-tuned for pedestrian and vehicle detection, demonstrates impressive processing speeds of 52 ms/frame on the HI3516EV200 chip and 66 ms/frame on the MSC313E chip, with minimal accuracy compromise. Despite a substantial reduction in parameter count compared to that in MobileNetV2, our model’s top-1 accuracy only slightly decreases by 2.6%, showcasing the effectiveness of our optimization technique. Our findings highlight the potential and applicability of our method in enhancing the performance and utility of IoT and embedded devices within smart cities. By achieving an optimal balance between computational efficiency and detection accuracy, our approach offers a promising avenue for advancing the capabilities of low-power devices in urban surveillance, traffic management, and other smart city applications, thereby contributing to the development of more intelligent, efficient, and responsive urban environments.