Traditional crowd-counting networks suffer from information loss when feature maps are reduced by pooling layers, leading to inaccuracies in counting crowds at a distance. Existing methods often assume correct annotations during training, disregarding the impact of noisy annotations, especially in crowded scenes. Furthermore, using a fixed Gaussian density model does not account for the varying pixel distribution of the camera distance. To overcome these challenges, we propose a Scale-Aware Crowd Counting Network (SACC-Net) that introduces a scale-aware loss function with error-compensation capabilities of noisy annotations. For the first time, we simultaneously model labeling errors (mean) and scale variations (variance) by spatially varying Gaussian distributions to produce fine-grained density maps for crowd counting. Furthermore, the proposed scale-aware Gaussian density model can be dynamically approximated with a low-rank approximation, leading to improved convergence efficiency with comparable accuracy. To create a smoother scale-aware feature space, this paper proposes a novel Synthetic Fusion Module (SFM) and an Intra-block Fusion Module (IFM) to generate fine-grained heat maps for better crowd counting. The lightweight version of our model, named SACC-LW, enhances the computational efficiency while retaining accuracy. The superiority and generalization properties of scale-aware loss function are extensively evaluated for different backbone architectures and performance metrics on six public datasets: UCF-QNRF, UCF CC 50, NWPU, ShanghaiTech A, ShanghaiTech B, and JHU. Experimental results also demonstrate that SACC-Net outperforms all state-of-the-art methods, validating its effectiveness in achieving superior crowd-counting accuracy. The source code is available at https://github.com/Naughty725.

Cracks on road surfaces pose serious risks to both pedestrians and drivers. Traditional manual crack detection methods are not only slow but also pose safety risks. Automating this process has the potential to greatly enhance detection efficiency and consequently improve driving safety. Although previous methods have shown promise in road crack detection, they often neglect interactions between multiple scales, causing smaller cracks to be overlooked in later stages of detection. This paper introduces the Cross-scale Overlapping Patch-based attention Network (COP-Net), which incorporates two critical components: the Scale-aware Channel Attention (SCA) module and the Patch-based Cross-scale Attention (PCA) module for crack detection. These innovations enable dynamic inference on multiple scales, resulting in a significant improvement in crack detection and segmentation. Notably, our approach excels at detecting both small and large cracks simultaneously. To validate the effectiveness of our approach, we conducted evaluations on three open datasets: CRACK500, CFD, and AEL. These evaluation results demonstrate that COP-Net surpasses eleven comparison methods, including HED, DeepCrack, UHDN, SSGNet, MFANet, FPHBN, DeepCrack, PBNet, PAFNet, CarNet, and SegFormer. Our model achieves new State-of-The-Art (SoTA) performance levels in terms of segmentation metrics such as AIU, ODS, and OIS.

Few-Shot Learning (FSL) has attracted growing attention in computer vision due to its capability in model training without the need for excessive data. FSL is challenging because the training and testing categories (the base vs. novel sets) can be largely diversified. Conventional transfer-based solutions that aim to transfer knowledge learned from large labeled training sets to target testing sets are limited, as critical adverse impacts of the shift in task distribution are not adequately addressed. In this paper, we extend the solution of transfer-based methods by incorporating the concept of metric-learning and channel attention. To better exploit the feature representations extracted by the feature backbone, we propose Class-Specific Channel Attention (CSCA) module, which learns to highlight the discriminative channels in each class by assigning each class one CSCA weight vector. Unlike general attention modules designed to learn globalclass features, the CSCA module aims to learn local and class-specific features with very effective computation. We evaluated the performance of the CSCA module on standard benchmarks including miniImagenet, Tiered-ImageNet, CIFAR-FS, and CUB-200-2011. Experiments are performed in inductive and in/cross-domain settings. We achieve new state-of-the-art results.

The Artificial Intelligence of Things (AIoT) plays a crucial role in shrimp farming by enabling automated and real-time monitoring of shrimp counting, especially larvae. With this counting information, proper feeding control can be maintained by equipping IoT devices to capture real-time data on water quality, temperature, and other environmental factors, ensuring healthy shrimp growth and increasing production. Taking advantage of the power of AIoT, this article proposes a scaled multilayer feature fusion network (SMILES-Net) to allow farmers to remotely and automatically manage the counting of shrimps, effectively reducing the need for manual labor, and enabling swift interventions to sustain shrimp well-being. Since shrimp larvae are extremely small, we frame this counting problem as a density prediction problem, where the sum of the constructed density map is the total number of shrimps predicted. The pooling operation used in convolutional neural networks scales each feature map to 1/4 and causes the rich features of small shrimps to disappear dramatically. To tackle this truncation problem, we propose a novel synthetic fusion module (SFM) and an intrablock fusion module (IFM) to create a smoother scale space, generating better heat maps with fine-grained features for shrimp counting. Furthermore, we introduce a lightweight version of SMILES-Net (LW-SMILES-Net) that enables real-time shrimp counting without compromising accuracy. This method is evaluated on different data sets for shrimp counting and outperforms all State-of-The-Art methods. Overall, integrating SMILES-Net with IoT devices can provide a powerful solution for real-time shrimp larvae counting in shrimp farming, contributing to sustainable increases in shrimp production. The data set is available athttps://github.com/Naughty725/shrimp.

Change detection (CD) aims to find the difference between two images at different times and output a change map to represent whether the region has changed or not. To achieve a better result in generating the change map, many State-of-The-Art (SoTA) methods design a deep learning model that has a powerful discriminative ability. However, these methods still get lower performance because they ignore spatial information and scaling changes between objects, giving rise to blurry boundaries. In addition to these, they also neglect the interactive information of two different images. To alleviate these problems, we propose our network, the Scale and Relation-Aware Siamese Network (SARAS-Net) to deal with this issue. In this paper, three modules are proposed that include relation-aware, scale-aware, and cross-transformer to tackle the problem of scene change detection more effectively. To verify our model, we tested three public datasets, including LEVIR-CD, WHU-CD, and DSFIN, and obtained SoTA accuracy. Our code is available at https://github.com/f64051041/SARAS-Net.

It is challenging to classify patterns with small inter-class variations but large intra-class variations especially for textured objects with relatively small sizes and blurry boundaries. We propose the Co-Feature Network (COFENet), a novel deep learning network for fine-grained texture-based image classification. State-of-the-art (SoTA) methods on this mostly rely on feature concatenation by merging convolutional features into fully connected layers. Some existing work explored the variation between pair-wise features during learning, they only considered the relations in the feature channels, and did not explore the spatial or structural relations among the image regions where the features are extracted from. We propose to leverage such information among the features and their relative spatial layouts to capture richer pairwise, orientationwise, and distancewise relations among feature channels for end-to-end learning of intra-class and inter-class variations.