Enhanced thyroid nodule detection and diagnosis: a mobile-optimized DeepLabV3+ approach for clinical deployments.

Synthetic Aperture Radar (SAR) can provide rich feature information under all-weather and day-night conditions because it is not affected by climatic conditions. However, multiplicative speckle noise exists in SAR images, which makes it difficult to accurately identify some fuzzy targets in SAR images, such as roads and rivers, during semantic segmentation. This paper proposes an improved Deeplabv3+ network that can be effectively applied to the semantic segmentation task of SAR images. Firstly, this paper added the attention mechanism and, combined with the idea of an image pyramid, proposed the Feature Post-Processing Module (FPPM) to post-process the network output feature map, obtain better fine image features, and solve the problem of fuzzy texture and spectral features of SAR images. Compared to the original Deeplabv3+ network, the segmentation accuracy has been improved by 3.64% and mIoU improved by 1.09%. Secondly, to solve the problems of limited SAR image data and an unbalanced sample, this paper used the focal loss function to improve the backbone function of the network, which increased the mIoU by 1.01%. Finally, the Atrous Spatial Pyramid Pooling (ASPP) module was improved and the 3 × 3 void convolution in ASPP was decomposed into 2D, which can maintain the void ratio and effectively reduce the calculation amount of the module, shorten the training time by 19 ms and improve the semantic segmentation effect.

Target detection based on unmanned aerial vehicle (UAV) images has increasingly become a hot topic with the rapid development of UAVs and related technologies. UAV aerial images often feature a large number of small targets and complex backgrounds due to the UAV’s flying height and shooting angle of view. These characteristics make the advanced YOLOv4 detection method lack outstanding performance in UAV aerial images. In light of the aforementioned problems, this study adjusted YOLOv4 to the image’s characteristics, making the improved method more suitable for target detection in UAV aerial images. Specifically, according to the characteristics of the activation function, different activation functions were used in the shallow network and the deep network, respectively. The loss for the bounding box regression was computed using the EIOU loss function. Improved Efficient Channel Attention (IECA) modules were added to the backbone. At the neck, the Spatial Pyramid Pooling (SPP) module was replaced with a pyramid pooling module. At the end of the model, Adaptive Spatial Feature Fusion (ASFF) modules were added. In addition, a dataset of forklifts based on UAV aerial imagery was also established. On the PASCAL VOC, VEDAI, and forklift datasets, we ran a series of experiments. The experimental results reveal that the proposed method (YOLO-DRONE, YOLOD) has better detection performance than YOLOv4 for the aforementioned three datasets, with the mean average precision (mAP) being improved by 3.06%, 3.75%, and 1.42%, respectively.

The purpose of human object detection is to obtain the number of people and their position in images, which is one of the core problems in the field of machine vision. However, the high missing detection rate from small- and medium-sized human bodies due to the large variety of human scale in human object detection tasks still influences the performance of human object detection. To solve the above problem, this paper proposed an improved ASPP_BiFPN_YOLOv4 (ABYOLOv4) method to detect human object detection. In detail, Atrous Spatial Pyramid Pooling (ASPP) module was used to replace the original Spatial Pyramid Pooling module to increase the receptive field level of the network and improve the perception ability of multi-scale targets. Then, the original Path Aggregation Network (PANet) multi-scale fusion module was replaced by the self-built bi-layer bidirectional feature pyramid network (Bi-FPN). Meanwhile, a new feature was imported into the proposed model to reuse the mid- and low-level features, which could enhance the ability of the network to express the characteristics of small- and medium-sized targets. Finally, the standard convolution in Bi-FPN was replaced by depth-separable convolution to make the network achieve the balance of accuracy and the number of parameters. To identify the performance of the proposed ABYOLOv4 model, the human object detection experiment is carried out by using the public data set of VOC2007 and VOC2012, the improved YOLOv4 algorithm is 0.5% higher than the original AP algorithm, and the weight file size of the model is reduced by 45.3 M. The experimental results demonstrated that the proposed ABYOLOv4 network has higher accuracy and lower computational cost for human target detection.

In light of recent advances in deep learning and Synthetic Aperture Radar (SAR) technology, there has been a growing adoption of ship detection models that are based on deep learning methodologies. However, the efficiency of SAR ship detection models is significantly impacted by complex backgrounds, noise, and multi-scale ships (the number of pixels occupied by ships in SAR images varies significantly). To address the aforementioned issues, this research proposes a Pyramid Pooling Attention Network (PPA-Net) for SAR multi-scale ship detection. Firstly, a Pyramid Pooled Attention Module (PPAM) is designed to alleviate the influence of background noise on ship detection while its parallel component favors the processing of multiple ship sizes. Different from the previous attention module, the PPAM module can better suppress the background noise in SAR images because it considers the saliency of ships in SAR images. Secondly, an Adaptive Feature Balancing Module (AFBM) is developed, which can automatically balance the conflict between ship semantic information and location information. Finally, the detection capabilities of the ship detection model for multi-scale ships are further improved by introducing the Atrous Spatial Pyramid Pooling (ASPP) module. This innovative module enhances the detection model’s ability to detect ships of varying scales by extracting features from multiple scales using atrous convolutions and spatial pyramid pooling. PPA-Net achieved detection accuracies of 95.19% and 89.27% on the High-Resolution SAR Images Dataset (HRSID) and the SAR Ship Detection Dataset (SSDD), respectively. The experimental results demonstrate that PPA-Net outperforms other ship detection models.

Recently, semantic segmentation has been widely used in text detection tasks and many excellent text detection methods have been proposed. They usually adopt deep convolutional neural network with consecutive striding or pooling operations to obtain a larger receptive field. However, that would lead to the lack of context which is crucial for text detection. In this paper, we propose an end-to-end trainable neural network to directly detect text regions without redundant stage other than a locality-aware non-maximum suppression is involved. We introduce atrous convolution in the backbone network to enlarge the receptive field, retaining more context information while controlling the spatial resolution of feature maps. The Atrous Spatial Pyramid Pooling (ASPP) module is attached on top of the feature maps to effectively detect texts of multiple scales. We have benchmarked our algorithm on three public datasets. It achieves highly competitive results in terms of text localization precision. More specifically, on the MSRA-TD500 datasets, the proposed algorithm achieves an F-score of 0.813, outperforming the previous best by a large margin.

This paper presents an enhancement to the DeepLabV3+ model for semantic segmentation by incorporating KolmogorovArnold Network (KAN) convolutions into its Atrous Spatial Pyramid Pooling (ASPP) module. KAN convolutions employ learnable B-spline activation functions, providing a flexible and adaptive framework for capturing complex features. In specific, unlike Multi-Layer Perceptron(MLP) with a fixed activation functions, the B-spline activation functions in KAN convolutions are able to dynamically fit to the data during training, which allows the network to capture intricate, multi-scale patterns from images more effectively. Additionally, KAN convolutions utilize adjustable grid granularity. Finer grids enable the model to extract more detailed feature in order to improve the improve segementation performance for objects of varying complexities. Experiment conducted on the PASCAL VOC 2012 dataset demonstrate a notable improvement, with the mean Intersection over Union (mIoU) increasing from 53.38% to 58.41%. Additionally, the modified model exhibits faster convergence and lower training loss compared to the original DeepLabV3+ model. These results highlight the potential of KAN convolutions to enhance semantic segmentation tasks, though the increased computational cost remains a consideration for future optimization.

In Earth observation, cloud severely affects the interpretation of optical satellites generated high-resolution images. Cloud-free optical images are vital for downstream tasks such as semantic segmentation and object detection. Thus, the elimination of clouds from optical imagery has emerged as a significant topic in remote sensing. Currently, most existing methods are proposed to leverage the texture information from auxiliary synthetic aperture radar (SAR) images to restore cloud-free images via direct channel merging. However, such a unified feature extraction approach often neglects the inherent distribution disparity between SAR and optical images—the result of differing imaging principles-potentially leading to significant feature loss. To this end, we introduce a network by jointing SAR and optical images multimodal and cross-attention network (MMCANet) to effectively extract multiscale contextual features from SAR imagery and integrate them with optical features. Specifically, instead of simple concatenation of the channels of SAR and optical images, we obtain high-dimensional features from them through independent feature extractors. The integration of these features is facilitated by a cross-attention mechanism that provides a more fine-grained amalgamation of information. Meanwhile, an atrous spatial pyramid pooling (ASPP) module is introduced into the integration of high-level features, which captures multiscale contextual information around clouded areas. In addition, we propose four advanced remote sensing image restoration algorithms that approach image restoration as a series of subtasks, gradually eliminating clouds to enhance performance. Comprehensive assessments show that MMCANet performs well on the SEN 12 MS-CR dataset with peak signal-to-noise ratio (PSNR) of 39.8871, structural similarity index (SSIM) of 0.9672, mean absolute error (MAE) of 0.0081, and spectral angle mapper (SAM) of 2.9884.

Palm oil is one of the most important commodities for Malaysia's economy. As the second-largest exporter of palm oil in the world, the government has set up various rules and regulations to promote sustainable plantations. Yet, some parties will take advantage of the rules by expanding their plantation areas beyond the permitted size. Thus, a remote sensing approach to automatically monitor the plantation size is proposed in this paper by using a deep neural network segmentation method. The spatial pyramid pooling (SPP) module is integrated with the well known U-Net architecture to improve the segmentation accuracy. Several variants of U-Net with SPP module are explored through varying the kernel size used in downsampling the input layer. The SPP module is placed right before the bottleneck block between the encoder and decoder sides of the network. The results show that the best accuracy is obtained by using U-Net with SPP of kernel sizes 2, 7 and 14. The proposed method has increased the accuracy from 0.7641 to 0.8152 when tested on Kaggle WiDS Dataset. The increment in performance is attributed to SPP ability in handling various scales input, which is a normal occurrence when the tested images cover a wide range of plantation ages that include young to mature trees.

While deep learning-based methods have gained considerable improvements in remote sensing (RS) image change detection (CD), scale variations and pseudochanges hinder most supervised methods’ performance. The CD networks derived from other fields can be fronted with false alarms and miss detections in high-resolution RS images due to the weak feature representation ability. In this article, an attention-guided end-to-end change detection network (AGCDetNet) is proposed based on the fully convolutional network and attention mechanism. AGCDetNet learns to enhance the feature representation of change information and achieve accuracy improvements using spatial attention and channel attention. A spatial attention module (SPAM) promotes the discrimination between the changed objects and the background by adding the learned spatial attention to the deep features. Channelwise attention-guided interference filtering unit (CIFU)/atrous spatial pyramid pooling (CG-ASPP) module enhances the representation of multilevel features and multiscale context, respectively. Extensive experiments have been conducted on several public datasets for performance evaluation, including LEVIR-CD, WHU, Season-Varying, WV2, and ZY3. Experiment results demonstrate that AGCDetNet outperforms several state-of-the-art methods of accuracy and robustness. Specifically, AGCDetNet achieves the best F1-score on two datasets, i.e., LEVIR-CD (0.9076) and Season-Varying (0.9654).

Traditional DeepLabV3+ image semantic segmentation methods face challenges in pitaya orchard environments characterized by multiple interference factors, complex image backgrounds, high computational complexity, and extensive memory consumption. This paper introduces an improved visual navigation path recognition method for pitaya orchards. Initially, DeepLabV3+ utilizes a lightweight MobileNetV2 as its primary feature extraction backbone, which is augmented with a Pyramid Split Attention (PSA) module placed after the Atrous Spatial Pyramid Pooling (ASPP) module. This improvement enhances the spatial feature representation of feature maps, thereby sharpening the segmentation boundaries. Additionally, an Efficient Channel Attention Network (ECANet) mechanism is integrated with the lower-level features of MobileNetV2 to reduce computational complexity and refine the clarity of target boundaries. The paper also designs a navigation path extraction algorithm, which fits the road mask regions segmented by the model to achieve precise navigation path recognition. Experimental findings show that the enhanced DeepLabV3+ model achieved a Mean Intersection over Union (MIoU) and average pixel accuracy of 95.79% and 97.81%, respectively. These figures represent increases of 0.59 and 0.41 percentage points when contrasted with the original model. Furthermore, the model’s memory consumption is reduced by 85.64%, 84.70%, and 85.06% when contrasted with the Pyramid Scene Parsing Network (PSPNet), U-Net, and Fully Convolutional Network (FCN) models, respectively. This reduction makes the proposed model more efficient while maintaining high segmentation accuracy, thus supporting enhanced operational efficiency in practical applications. The test results for navigation path recognition accuracy reveal that the angle error between the navigation centerline extracted using the least squares method and the manually fitted centerline is less than 5°. Additionally, the average deviation between the road centerlines extracted under three different lighting conditions and the actual road centerline is only 2.66 pixels, with an average image recognition time of 0.10 s. This performance suggests that the study can provide an effective reference for visual navigation in smart agriculture.

Natural breeding scenes have the characteristics of a large number of cows, complex lighting, and a complex background environment, which presents great difficulties for the detection of dairy cow estrus behavior. However, the existing research on cow estrus behavior detection works well in ideal environments with a small number of cows and has a low inference speed and accuracy in natural scenes. To improve the inference speed and accuracy of cow estrus behavior in natural scenes, this paper proposes a cow estrus behavior detection method based on the improved YOLOv5. By improving the YOLOv5 model, it has stronger detection ability for complex environments and multi-scale objects. First, the atrous spatial pyramid pooling (ASPP) module is employed to optimize the YOLOv5l network at multiple scales, which improves the model’s receptive field and ability to perceive global contextual multiscale information. Second, a cow estrus behavior detection model is constructed by combining the channel-attention mechanism and a deep-asymmetric-bottleneck module. Last, K-means clustering is performed to obtain new anchors and complete intersection over union (CIoU) is used to introduce the relative ratio between the predicted box of the cow mounting and the true box of the cow mounting to the regression box prediction function to improve the scale invariance of the model. Multiple cameras were installed in a natural breeding scene containing 200 cows to capture videos of cows mounting. A total of 2668 images were obtained from 115 videos of cow mounting events from the training set, and 675 images were obtained from 29 videos of cow mounting events from the test set. The training set is augmented by the mosaic method to increase the diversity of the dataset. The experimental results show that the average accuracy of the improved model was 94.3%, that the precision was 97.0%, and that the recall was 89.5%, which were higher than those of mainstream models such as YOLOv5, YOLOv3, and Faster R-CNN. The results of the ablation experiments show that ASPP, new anchors, C3SAB, and C3DAB designed in this study can improve the accuracy of the model by 5.9%. Furthermore, when the ASPP dilated convolution was set to (1,5,9,13) and the loss function was set to CIoU, the model had the highest accuracy. The class activation map function was utilized to visualize the model’s feature extraction results and to explain the model’s region of interest for cow images in natural scenes, which demonstrates the effectiveness of the model. Therefore, the model proposed in this study can improve the accuracy of the model for detecting cow estrus events. Additionally, the model’s inference speed was 71 frames per second (fps), which meets the requirements of fast and accurate detection of cow estrus events in natural scenes and all-weather conditions.

Recent progress in salient object detection (SOD) mainly depends on the Atrous Spatial Pyramid Pooling (ASPP) module for multi-scale learning. Intuitively, different input images, different pixels, and different network layers may have different preferences for various feature scales. However, ASPP treats all feature scales as equally important by a simple sum operation. To this end, we propose Attentive Atrous Spatial Pyramid Pooling (A2SPP) by adding a new Cubic Information-Embedding Attention (CIEA) module at each branch of ASPP. In this way, each position in the 3D feature map can automatically learn the feature scales it prefers. Specifically, CIEA consists of Spatial-Embedding Channel Attention (SECA) and Channel-Embedding Spatial Attention (CESA). Instead of the previous direct squeeze and ignoring of one dimension when computing the attention for the other dimension, SECA/CESA attempts to embed spatial/channel information into channel/spatial attention, respectively. In addition, CIEA learns SECA and CESA for each 3D position simultaneously rather than previous separate computation of channel and spatial attention for each 2D position. Incorporating A2SPP and CIEA, the proposed A2SPPNet performs favorably against previous state-of-the-art SOD methods.

This study aimed to investigate the effect of an image denoising algorithm based on weighted low-rank matrix restoration on thyroid nodule ultrasound images. A total of 1000 original ultrasound image data sets of thyroid nodules were selected as the study samples. The nodule segmentation data set of thyroid ultrasound region of interest (ROI) images was drawn and acquired. By introducing multiscale features and an attention mechanism to optimize the U-Net model, an ultrasound image segmentation model (F-U-Net) was constructed. The performance of the traditional U network model and full convolutional neural network model (FCN) was analyzed and compared by simulation experiments. The results showed that the dice coefficient, accuracy, and recall of the improved loss function in this study were significantly higher than those of the traditional cross entropy loss function and dice coefficient loss function, and the differences were statistically significant (P < 0.05). The Dice coefficient, accuracy, and recall of the F-U-net model were significantly higher than those of the traditional FCN model and U-net model (P < 0.05). The diagnostic sensitivity, specificity, accuracy, and positive predictive value of the F-U-net model for benign and malignant thyroid nodules were significantly higher than those of the FCN model and U-net model (P < 0.05). In summary, the proposed F-U network can effectively process the ultrasound images of thyroid nodules, improve the image quality, and help to improve the diagnostic effect of benign and malignant thyroid nodules. It provides a data reference for segmentation and reconstruction of benign and malignant ultrasound images of thyroid nodules.

At present, medical image segmentation is a challenging and developing task in medical image field, in which semantic segmentation algorithms play an important role in many clinical applications. The atrous spatial pyramid pooling (ASPP) module of Deeplab-V3 can well balance the diversity, robustness, and connectivity of global feature map extraction under different scenarios. However, there is still a large improving prospect for medical image segmentation. Therefore, we introduce mechanisms of multi-group parallel processing, mass guided loss focusing on irregular shape, and k-nearest attention architecture, respectively, to achieve higher robustness and stronger global connectivity with more sufficient receptive field compared to original methods. The proposed model effectively alleviates the deficiencies of local semantic receptive fields, improves the ability of generalization of our model, and overcomes the agnosticism of feature spatial distribution. Under the premise of maintaining feature diversity and enhancing heterogeneous information correlation, we obtain better segmentation performance in our experiments by enriching global feature representations. We conduct a series of segmentation experiments using the proposed model and on medical image datasets: STARE, CHASE, DRIVE and HRF. Compared with Deeplab-V3 baseline, the performance is significantly improved in terms of several metrics, which verifies the effectiveness of our method.

Coronavirus disease (COVID‐19) affects the lives of billions of people worldwide and has destructive impacts on daily life routines, the global economy, and public health. Early diagnosis and quantification of COVID‐19 infection have a vital role in improving treatment outcomes and interrupting transmission. For this purpose, advances in medical imaging techniques like computed tomography (CT) scans offer great potential as an alternative to RT‐PCR assay. CT scans enable a better understanding of infection morphology and tracking of lesion boundaries. Since manual analysis of CT can be extremely tedious and time‐consuming, robust automated image segmentation is necessary for clinical diagnosis and decision support. This paper proposes an efficient segmentation framework based on the modified DeepLabV3+ using lower atrous rates in the Atrous Spatial Pyramid Pooling (ASPP) module. The lower atrous rates make receptive small to capture intricate morphological details. The encoder part of the framework utilizes a pre‐trained residual network based on dilated convolutions for optimum resolution of feature maps. In order to evaluate the robustness of the modified model, a comprehensive comparison with other state‐of‐the‐art segmentation methods was also performed. The experiments were carried out using a fivefold cross‐validation technique on a publicly available database containing 100 single‐slice CT scans from >40 patients with COVID‐19. The modified DeepLabV3+ achieved good segmentation performance using around 43.9 M parameters. The lower atrous rates in the ASPP module improved segmentation performance. After fivefold cross‐validation, the framework achieved an overall Dice similarity coefficient score of 0.881. The results demonstrate that several minor modifications to the DeepLabV3+ pipeline can provide robust solutions for improving segmentation performance and hardware implementation.