Spatial Attention Module Research Articles

A Deep Learning-Based Preprocessing Method for Single Interferometric Fringe Patterns

A novel preprocessing method based on a modified U-NET is proposed for single interference fringes. The framework is constructed by introducing spatial attention and channel attention modules to optimize performance. In this process, interferometric fringe maps with an added background intensity, fringe amplitude, and ambient noise are used as the input to the network, which outputs fringe maps in an ideal state. Simulated and experimental results demonstrated that this technique can preprocess single interference fringes in ~1 microsecond. The quality of the results was further evaluated using the root mean square error, peak signal-to-noise ratio, structural similarity, and equivalent number of views. The proposed method outperformed U-NET, U-NET++, and other conventional algorithms as measured by each of these metrics. In addition, the model produced high-quality normalized fringes by combining objective data with visual effects, significantly improving the accuracy of the phase solutions for single interference fringes.

A spatio-temporal fusion deep learning network with application to lightning nowcasting

Lightning is a rapidly evolving phenomenon, exhibiting both mesoscale and microscale characteristics. Its prediction significantly relies on timely and accurate data observation. With the implementation of new generation weather radar systems and lightning detection networks, radar reflectivity image products, and lightning observation data are becoming increasingly abundant. Research focus has shifted towards lightning nowcasting (prediction of imminent events), utilizing deep learning (DL) methods to extract lightning features from very large data sets. In this paper, we propose a novel spatio-temporal fusion deep learning lightning nowcasting network (STF-LightNet) for lightning nowcasting. The network is based on a 3-dimensional U-Net architecture with encoder-decoder blocks and adopts a structure of multiple branches as well as the main path for the encoder block. To address the challenges of feature extraction and fusion of multi-source data, multiple branches are used to extract different data features independently, and the main path fuses these features. Additionally, a spatial attention (SA) module is added to each branch and the main path to automatically identify lightning areas and enhance their features. The main path fusion is conducted in two steps: the first step fuses features from the branches, and the second fuses features from the previous and current levels of the main path using two different methodsthe weighted summation fusion method and the attention gate fusion method. To overcome the sparsity of lightning observations, we employ an inverse frequency weighted cross-entropy loss function. Finally, STF-LightNet is trained using observations from the previous half hour to predict lightning in the next hour. The outcomes illustrate that the fusion of both the multi-branch and main path structures enhances the network’s ability to effectively integrate features from diverse data sources. Attention mechanisms and fusion modules allow the network to capture more detailed features in the images.

PVswin-YOLOv8s: UAV-Based Pedestrian and Vehicle Detection for Traffic Management in Smart Cities Using Improved YOLOv8

In smart cities, effective traffic congestion management hinges on adept pedestrian and vehicle detection. Unmanned Aerial Vehicles (UAVs) offer a solution with mobility, cost-effectiveness, and a wide field of view, and yet, optimizing recognition models is crucial to surmounting challenges posed by small and occluded objects. To address these issues, we utilize the YOLOv8s model and a Swin Transformer block and introduce the PVswin-YOLOv8s model for pedestrian and vehicle detection based on UAVs. Firstly, the backbone network of YOLOv8s incorporates the Swin Transformer model for global feature extraction for small object detection. Secondly, to address the challenge of missed detections, we opt to integrate the CBAM into the neck of the YOLOv8. Both the channel and the spatial attention modules are used in this addition because of how well they extract feature information flow across the network. Finally, we employ Soft-NMS to improve the accuracy of pedestrian and vehicle detection in occlusion situations. Soft-NMS increases performance and manages overlapped boundary boxes well. The proposed network reduced the fraction of small objects overlooked and enhanced model detection performance. Performance comparisons with different YOLO versions ( for example YOLOv3 extremely small, YOLOv5, YOLOv6, and YOLOv7), YOLOv8 variants (YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l), and classical object detectors (Faster-RCNN, Cascade R-CNN, RetinaNet, and CenterNet) were used to validate the superiority of the proposed PVswin-YOLOv8s model. The efficiency of the PVswin-YOLOv8s model was confirmed by the experimental findings, which showed a 4.8% increase in average detection accuracy (mAP) compared to YOLOv8s on the VisDrone2019 dataset.

Multi-Scale Feature Fusion Attention Network for Building Extraction in Remote Sensing Images

The efficient semantic segmentation of buildings in high spatial resolution remote sensing images is a technical prerequisite for land resource management, high-precision mapping, construction planning and other applications. Current building extraction methods based on deep learning can obtain high-level abstract features of images. However, the extraction of some occluded buildings is inaccurate, and as the network deepens, small-volume buildings are lost and edges are blurred. Therefore, we introduce a multi-resolution attention combination network, which employs a multiscale channel and spatial attention module (MCAM) to adaptively capture key features and eliminate irrelevant information, which improves the accuracy of building extraction. In addition, we present a layered residual connectivity module (LRCM) to enhance the expression of information at different scales through multi-level feature fusion, significantly improving the understanding of context and the capturing of fine edge details. Extensive experiments were conducted on the WHU aerial image dataset and the Massachusetts building dataset. Compared with state-of-the-art semantic segmentation methods, this network achieves better building extraction results in remote sensing images, proving the effectiveness of the method.

An improved V-Net lung nodule segmentation model based on pixel threshold separation and attention mechanism.

Accurate labeling of lung nodules in computed tomography (CT) images is crucial in early lung cancer diagnosis and before nodule resection surgery. However, the irregular shape of lung nodules in CT images and the complex lung environment make it much more challenging to segment lung nodules accurately. On this basis, we propose an improved V-Net segmentation method based on pixel threshold separation and attention mechanism for lung nodules. This method first offers a data augment strategy to solve the problem of insufficient samples in 3D medical datasets. In addition, we integrate the feature extraction module based on pixel threshold separation into the model to enhance the feature extraction ability under different thresholds on the one hand. On the other hand, the model introduces channel and spatial attention modules to make the model pay more attention to important semantic information and improve its generalization ability and accuracy. Experiments show that the Dice similarity coefficients of the improved model on the public datasets LUNA16 and LNDb are 94.9% and 81.1% respectively, and the sensitivities reach 92.7% and 76.9% respectively. which is superior to most existing UNet architecture models and comparable to the manual level segmentation results by medical technologists.

Seismic data reconstruction based on a multicascade self-guided network

Due to inherent limitations in data acquisition, seismic data reconstruction is an important procedure to recover missing data or improve observation density. Many conventional methods exist to solve the reconstruction task. Reconstruction is challenging, especially in the case of complex seismic data. Recently, convolutional neural networks (CNN) have been applied in seismic data processing. In most cases, the architectures of these CNN-based methods are relatively simple, without sufficient feature interaction, limiting their performance. To improve reconstruction results, a multicascade self-guided network (MSG-Net) is presented. In general, MSG-Net is inspired by the self-guided scheme, and a multicascade architecture is designed to extract informative features within the analyzed seismic data at different resolutions. Following this, a parallel spatial attention module is used to further refine and enhance the primary features, thereby improving reconstruction accuracy. To test and verify the new approach, a training data set is generated, based on the synthetic records obtained by forward modeling methods. Experimental results demonstrate that MSG-Net is a promising approach for performing seismic data interpolation.

Audio-visual saliency prediction with multisensory perception and integration

Audio-visual saliency prediction (AVSP) is a task that aims to model human attention patterns in the perception of auditory and visual scenes. Given the challenges associated with perceiving and combining multi-modal saliency features from videos, this paper presents a multi-sensory framework for AVSP. This framework is designed to extract audio, motion and image saliency features and integrate them effectively, which can then serve as a general architecture for the AVSP task. To obtain multi-sensory information, we develop a three-stream encoder that extracts audio, motion and image saliency features. In particular, we utilize a pre-trained encoder with knowledge related to image saliency to extract saliency features for each frame. The image saliency features are then incorporated with motion features using a spatial attention module. For motion features, 3D convolutional neural networks (CNNs) like S3D are commonly used in AVSP models. However, these networks are unable to effectively capture the global motion relationship in videos. To tackle this problem, we incorporate Transformer- and MLP-based motion encoders into the AVSP models. To learn joint audio-visual representations, an audio-visual fusion block is exploited to enhance the correlation between audio and visual motion features under the supervision of a cosine similarity loss in a self-supervised manner. Finally, a multi-stage decoder integrates audio, motion and image saliency features to generate the final saliency map. We evaluate our methods on six audio-visual eye-tracking datasets. Experimental results demonstrate that our method achieves compelling performance compared to the state-of-the-art methods. The source code is available at https://github.com/oraclefina/MSPI.

Detection Technique Tailored for Small Targets on Water Surfaces in Unmanned Vessel Scenarios

Lightweight detection methods are frequently utilized for unmanned system sensing; however, to tackle the challenge of low precision in detecting small targets on the water’s surface by unmanned surface vessels, we present an enhanced method for ship target detection tailored specifically to this context. Building upon the mainstream single-stage Yolov8 object detection model, our approach involves the integration of the Reparameterized Convolutional Spatial Oversampling Attention (RCSOSA) module, replacing the traditional Classic 2D Convolutional (C2f) module to bolster the network’s feature extraction capabilities. Additionally, we introduce a downsampling module, Spatial to Depth Convolution (SPDConv), to amplify the extraction of features relevant to small targets, thereby enhancing detection accuracy. Finally, the Focal Modulation module, based on focal modulation, replaces the SPPF (Spatial Pyramid Pooling with FPN) module, leading to a reduction in channel count, parameter volume, and an augmentation of the network’s feature representation. Experimental results demonstrate that the proposed model achieves a 3.6% increase in mAP@0.5 and a 2.1% improvement in mAP@0.5:0.95 compared to the original Yolov8 model, while maintaining real-time processing capabilities. The research validates the higher accuracy and stronger generalization capabilities of the proposed improved ship target detection method in various complex water surface environments.

Dual-stream pyramid and attention network for stereo matching

In order to accurately estimate the disparity of ill-posed regions, such as weak texture and occlusion regions, we propose DSPANet, a stereo matching network that incorporates a dual-stream pyramid module and a channel and spatial attention module. The dual-stream pyramid module captures numerous complementary features from different layers by utilizing multi-resolution inputs and feature extraction blocks. This approach enables the learning of local detailed features at various scales. These features at various scales are then combined to calculate the stereo matching cost. By incorporating channel and spatial attention module into the feature extraction process to obtain richer and more concise contextual information, the matching cost can be constructed more accurately, which provides powerful conditions for subsequent cost aggregation. In the cost aggregation stage, we utilize the stacked hourglass module for both encoding and decoding. Additionally, we incorporate 3D global attention upsampling during the decoding stage, which enables high-level features to provide guidance information to low-level features in a simple way. We evaluate our proposed method on the Scene Flow dataset, as well as the KITTI2012 and KITTI2015 datasets. The experimental results demonstrate that our DSPANet achieves superior performance and effectively enhances the matching results in ill-posed regions. Our code has been implemented using PyTorch and will be released after paper publication at https://github.com/Shi-LiQing/DSPANet.

Exploiting multi-scale hierarchical feature representation for visual tracking

Convolutional neural networks (CNNs) have been the dominant architectures for feature extraction tasks, but CNNs do not look for and focus on some specific image features. Correlation operations play an important role in visual tracking. However, the correlation operation reserves a large amount of unfavorable background information. In this paper, we propose an effective feature recognizer including channel and spatial attention modules to focus on important object feature information. Thus, the representation power of the feature extraction network is improved. Further, we design a multi-scale feature fusion network. The fusion network performs feature fusion on template feature and encoded feature branches to establish connections between features at different scales. Experiments on six benchmarks demonstrate that the proposed tracker outperforms the state-of-the-art trackers. In particular, the proposed tracker achieves an 80.4% AUC on TrackingNet and a 68.4% AUC on GOT-10k while running at a real-time speed.

CESA-MCFormer: An Efficient Transformer Network for Hyperspectral Image Classification by Eliminating Redundant Information.

Hyperspectral image (HSI) classification is a highly challenging task, particularly in fields like crop yield prediction and agricultural infrastructure detection. These applications often involve complex image types, such as soil, vegetation, water bodies, and urban structures, encompassing a variety of surface features. In HSI, the strong correlation between adjacent bands leads to redundancy in spectral information, while using image patches as the basic unit of classification causes redundancy in spatial information. To more effectively extract key information from this massive redundancy for classification, we innovatively proposed the CESA-MCFormer model, building upon the transformer architecture with the introduction of the Center Enhanced Spatial Attention (CESA) module and Morphological Convolution (MC). The CESA module combines hard coding and soft coding to provide the model with prior spatial information before the mixing of spatial features, introducing comprehensive spatial information. MC employs a series of learnable pooling operations, not only extracting key details in both spatial and spectral dimensions but also effectively merging this information. By integrating the CESA module and MC, the CESA-MCFormer model employs a "Selection-Extraction" feature processing strategy, enabling it to achieve precise classification with minimal samples, without relying on dimension reduction techniques such as PCA. To thoroughly evaluate our method, we conducted extensive experiments on the IP, UP, and Chikusei datasets, comparing our method with the latest advanced approaches. The experimental results demonstrate that the CESA-MCFormer achieved outstanding performance on all three test datasets, with Kappa coefficients of 96.38%, 98.24%, and 99.53%, respectively.

GSA-SiamNet: A Siamese Network with Gradient-Based Spatial Attention for Pan-Sharpening of Multi-Spectral Images

Pan-sharpening is a fusion process that combines a low-spatial resolution, multi-spectral image that has rich spectral characteristics with a high-spatial resolution panchromatic (PAN) image that lacks spectral characteristics. Most previous learning-based approaches rely on the scale-shift assumption, which may not be applicable in the full-resolution domain. To solve this issue, we regard pan-sharpening as a multi-task problem and propose a Siamese network with Gradient-based Spatial Attention (GSA-SiamNet). GSA-SiamNet consists of four modules: a two-stream feature extraction module, a feature fusion module, a gradient-based spatial attention (GSA) module, and a progressive up-sampling module. In the GSA module, we use Laplacian and Sobel operators to extract gradient information from PAN images. Spatial attention factors, learned from the gradient prior, are multiplied during the feature fusion, up-sampling, and reconstruction stages. These factors help to keep high-frequency information on the feature map as well as suppress redundant information. We also design a multi-resolution loss function that guides the training process under the constraints of both reduced- and full-resolution domains. The experimental results on WorldView-3 satellite images obtained in Moscow and San Juan demonstrate that our proposed GSA-SiamNet is superior to traditional and other deep learning-based methods.

A Lightweight Image Super-Resolution Reconstruction Algorithm Based on the Residual Feature Distillation Mechanism.

In recent years, the development of image super-resolution (SR) has explored the capabilities of convolutional neural networks (CNNs). The current research tends to use deeper CNNs to improve performance. However, blindly increasing the depth of the network does not effectively enhance its performance. Moreover, as the network depth increases, more issues arise during the training process, requiring additional training techniques. In this paper, we propose a lightweight image super-resolution reconstruction algorithm (SISR-RFDM) based on the residual feature distillation mechanism (RFDM). Building upon residual blocks, we introduce spatial attention (SA) modules to provide more informative cues for recovering high-frequency details such as image edges and textures. Additionally, the output of each residual block is utilized as hierarchical features for global feature fusion (GFF), enhancing inter-layer information flow and feature reuse. Finally, all these features are fed into the reconstruction module to restore high-quality images. Experimental results demonstrate that our proposed algorithm outperforms other comparative algorithms in terms of both subjective visual effects and objective evaluation quality. The peak signal-to-noise ratio (PSNR) is improved by 0.23 dB, and the structural similarity index (SSIM) reaches 0.9607.

Attention-Based Monocular Depth Estimation Considering Global and Local Information in Remote Sensing Images

Monocular depth estimation using a single remote sensing image has emerged as a focal point in both remote sensing and computer vision research, proving crucial in tasks such as 3D reconstruction and target instance segmentation. Monocular depth estimation does not require multiple views as references, leading to significant improvements in both time and efficiency. Due to the complexity, occlusion, and uneven depth distribution of remote sensing images, there are currently few monocular depth estimation methods for remote sensing images. This paper proposes an approach to remote sensing monocular depth estimation that integrates an attention mechanism while considering global and local feature information. Leveraging a single remote sensing image as input, the method outputs end-to-end depth estimation for the corresponding area. In the encoder, the proposed method employs a dense neural network (DenseNet) feature extraction module with efficient channel attention (ECA), enhancing the capture of local information and details in remote sensing images. In the decoder stage, this paper proposes a dense atrous spatial pyramid pooling (DenseASPP) module with channel and spatial attention modules, effectively mitigating information loss and strengthening the relationship between the target’s position and the background in the image. Additionally, weighted global guidance plane modules are introduced to fuse comprehensive features from different scales and receptive fields, finally predicting monocular depth for remote sensing images. Extensive experiments on the publicly available WHU-OMVS dataset demonstrate that our method yields better depth results in both qualitative and quantitative metrics.

SalDA: DeepConvNet Greets Attention for Visual Saliency Prediction

Predicting salient regions in images requires the capture of contextual information in the scene. Conventional saliency models typically use the encoder-decoder architecture and multi-scale feature fusion for modeling contextual features, which, however, possess huge computational cost and model parameters. In this paper, we address the saliency prediction task by capturing long-range dependencies based on the self-attention mechanism. Self-attention has been widely used in image recognition or other classification tasks, but is still rarely being considered in regression based saliency prediction task. Inspired by non-local block, we propose a new SALiency prediction network in which DeepConvNet is integrated with the Attention mechanism, namely SalDA. Considering each feature map may capture different salient regions, our spatial attention module firstly adaptively aggregates the feature at each position by a weighted sum of the features at all positions within each independent channel. Meanwhile, in order to capture interdependence between channels, we also introduce a channel attention module to integrate different features among different channels. We combine these two attention modules into a multi-attention module to further improve the saliency map prediction for the network. We show the effectiveness of SalDA on the largest saliency prediction dataset SALICON. Compared to other state-of-the-art methods in this area, we can yield comparable saliency prediction performance, but with substantially less model parameters and shorter inference time.

Parallel Self-Attention and Spatial-Attention Fusion for Human Pose Estimation and Running Movement Recognition

Human pose estimation (HPE) is a fundamental yet promising visual recognition problem. Existing popular methods (e.g., Hourglass and its variants) either attempt to directly add local features element-wisely, or (e.g., vision transformers) try to learn the global relationships among different human parts. However, it remains an open problem to effectively integrate the local-global representations for accurate HPE. In this work, we design four feature fusion strategies on the hierarchical ResNet structure, including direct channel concatenation, element-wise addition and two parallel structures. Both two parallel structures adopt the naive self-attention encoder to model global dependencies. The difference between them is that one adopts the original ResNet BottleNeck while the other employs a spatial-attention module (named SSF) to learn the local patterns. Experiments on COCO Keypoint 2017 show that our SSF for HPE (named SSPose) achieves the best average precision with acceptable computational cost among the compared state-of-the-art methods. In addition, we build a lightweight running dataset to verify the effectiveness of SSPose. Based solely on the keypoints estimated by our SSPose, we propose a regression model to identify valid running movements without training any other classifiers. Our source codes and running dataset are publicly available.

Agricultural development driven by the digital economy: improved EfficientNet vegetable quality grading

IntroductionThe conventional manual grading of vegetables poses challenges that necessitate innovative solutions. In this context, our paper proposes a deep learning methodology for vegetable quality grading.MethodsTo address the scarcity of vegetable datasets, we constructed a unique dataset comprising 3,600 images of diverse vegetables, including lettuce, broccoli, tomatoes, garlic, bitter melon, and Chinese cabbage. We present an improved CA-EfficientNet-CBAM model for vegetable quality grading. The CA module replaces the squeeze-and-excitation (SE) module in the MobileNet convolution (MBConv) structure of the EfficientNet model. Additionally, a channel and spatial attention module (CBAM) is integrated before the final layer, accelerating model training and emphasizing nuanced features.ResultsThe enhanced model, along with comparisons to VGGNet16, ResNet50, and DenseNet169, was subjected to ablation experiments. Our method achieved the highest classification accuracy of 95.12% on the cabbage vegetable image test set, outperforming VGGNet16, ResNet50, and DenseNet169 by 8.34%, 7%, and 4.29%, respectively. Notably, the proposed method effectively reduced the model’s parameter count.DiscussionOur experimental results highlight the effectiveness of the deep learning approach in improving vegetable quality grading accuracy. The superior performance of the enhanced EfficientNet model underscores its potential for advancing the field, achieving both high classification accuracy and parameter efficiency. We hope this aligns with your expectations. If there are further adjustments or clarifications needed, please let us know.

Attention-Based Ensemble Network for Effective Breast Cancer Classification over Benchmarks

Globally, breast cancer (BC) is considered a major cause of death among women. Therefore, researchers have used various machine and deep learning-based methods for its early and accurate detection using X-ray, MRI, and mammography image modalities. However, the machine learning model requires domain experts to select an optimal feature, obtains a limited accuracy, and has a high false positive rate due to handcrafting features extraction. The deep learning model overcomes these limitations, but these models require large amounts of training data and computation resources, and further improvement in the model performance is needed. To do this, we employ a novel framework called the Ensemble-based Channel and Spatial Attention Network (ECS-A-Net) to automatically classify infected regions within BC images. The proposed framework consists of two phases: in the first phase, we apply different augmentation techniques to enhance the size of the input data, while the second phase includes an ensemble technique that parallelly leverages modified SE-ResNet50 and InceptionV3 as a backbone for feature extraction, followed by Channel Attention (CA) and Spatial Attention (SA) modules in a series manner for more dominant feature selection. To further validate the ECS-A-Net, we conducted extensive experiments between several competitive state-of-the-art (SOTA) techniques over two benchmarks, including DDSM and MIAS, where the proposed model achieved 96.50% accuracy for the DDSM and 95.33% accuracy for the MIAS datasets. Additionally, the experimental results demonstrated that our network achieved a better performance using various evaluation indicators, including accuracy, sensitivity, and specificity among other methods.

Improved foreign object tracking algorithm in coal for belt conveyor gangue selection robot with YOLOv7 and DeepSORT

Given the indistinct dissimilarities between foreign matter and coal in terms of their physical characteristics, the utilization of machine vision detection technology for foreign matter tracking yields suboptimal accuracy and precision, thereby failing to satisfy the exigencies of coal mine production. In this paper, we proffer an enhanced you only look once version 7 (YOLOv7) and simple online and realtime tracking with a deep association metric (DeepSORT) algorithm for the purpose of tracking foreign entities in the coal domain. The YOLOv7 network model undergoes enhancements through the reduction of Backbone convolutional layers, the introduction of the context overlap and transition network (COTN) module, and the incorporation of a compact target detection layer. Concurrently, the DeepSORT tracking algorithm is refined by substituting the re-recognition network structure of DeepSORT with the machine translation interface (MTL) framework and replacing the DeepSORT foreign object tracking algorithm with the occlusion-aware spatial attention (OSA) module. Empirical findings substantiate the efficacy of the proposed algorithm, as it successfully achieves foreign object detection and tracking. Specifically, the algorithm attains a foreign object detection accuracy of 91.3% and a recall rate of 90.6%. In addition, it achieves a tracking accuracy of 76.1% for multiple object tracking accuracy (MOTA), and a precision rate of 80.6% for multiple object tracking precision (MOTP). Notably, in comparison to the DeepSORT tracking algorithm, the proposed algorithm exhibits a significant improvement of 6 percentage points in MOTA and 3.9 percentage points in MOTP.

MAFE-Net: retinal vessel segmentation based on a multiple attention-guided fusion mechanism and ensemble learning network.

The precise and automatic recognition of retinal vessels is of utmost importance in the prevention, diagnosis and assessment of certain eye diseases, yet it brings a nontrivial uncertainty for this challenging detection mission due to the presence of intricate factors, such as uneven and indistinct curvilinear shapes, unpredictable pathological deformations, and non-uniform contrast. Therefore, we propose a unique and practical approach based on a multiple attention-guided fusion mechanism and ensemble learning network (MAFE-Net) for retinal vessel segmentation. In conventional UNet-based models, long-distance dependencies are explicitly modeled, which may cause partial scene information loss. To compensate for the deficiency, various blood vessel features can be extracted from retinal images by using an attention-guided fusion module. In the skip connection part, a unique spatial attention module is applied to remove redundant and irrelevant information; this structure helps to better integrate low-level and high-level features. The final step involves a DropOut layer that removes some neurons randomly to prevent overfitting and improve generalization. Moreover, an ensemble learning framework is designed to detect retinal vessels by combining different deep learning models. To demonstrate the effectiveness of the proposed model, experimental results were verified in public datasets STARE, DRIVE, and CHASEDB1, which achieved F1 scores of 0.842, 0.825, and 0.814, and Accuracy values of 0.975, 0.969, and 0.975, respectively. Compared with eight state-of-the-art models, the designed model produces satisfactory results both visually and quantitatively.