Research on Improved Road Visual Navigation Recognition Method Based on DeepLabV3+ in Pitaya Orchard

Environmental protection is still a key issue that cannot be ignored at this stage of social development. With the development of artificial intelligence, various technologies increasingly tend to be widely used in the field of environmental protection, such as searching the wilderness through an unmanned aerial vehicle (UAV) and cleaning garbage by robots. Traditional object detection algorithms for this scenario suffer from low accuracy and high computational cost. Therefore, this paper proposes an algorithm applied to automatic garbage detection and instance segmentation in complex scenes. First, we construct sample-fused feature pyramid networks (SF-FPN) to achieve multi-scale feature sampling on multiple levels, to enhance the semantic representation of features. Second, adding the mask branch based on conditional convolution, introducing the idea of instance-filters to automatically generate the filter parameters of the Fully Convolutional Networks (FCN), to realize the instance-level pixel classification. Moreover, the Atrous Spatial Pyramid Pooling (ASPP) module is introduced to encode the feature information in a dense way to assist the generation of MASK. Finally, the object is detected and the instance is segmented by a two-branch structure. In addition, we also perform data augmentation on the original dataset to prevent model overfitting. The proposed algorithm reaches 82.7 and 72.4 according to the mAP index of detection and instance segmentation while using the public TACO dataset.

Robust Prediction of Isodose Distribution with a Fully Convolutional Networks (FCN)-Based Deep Learning Model

This study aims to enhance the efficiency and accuracy of thyroid nodule segmentation in ultrasound images, ultimately improving nodule detection and diagnosis. For clinical deployment on mobile and embedded devices, DeepLabV3+ strives to achieve a balance between a lightweight architecture and high segmentation accuracy. A comprehensive dataset of ultrasound images was meticulously curated using a high-resolution ultrasound imaging device. Data acquisition adhered to standardized protocols to ensure high-quality imaging. Preprocessing steps, including noise reduction and contrast optimization, were applied to enhance image clarity. Expert radiologists provided ground truth labels through meticulous annotation. To improve segmentation performance, we integrated MobileNetV2 and Depthwise Separable Dilated Convolution into the Atrous Spatial Pyramid Pooling (ASPP) module, incorporating the Pyramid Pooling Module (PPM) and attention mechanisms. To mitigate classification imbalances, we employed Tversky loss functions in the ultrasound image classification process. In semantic image segmentation, DeepLabV3+ achieved an impressive Intersection over Union (IoU) of 94.37%, while utilizing only 12.4 MB of parameters, including weights and biases. This remarkable accuracy demonstrates the effectiveness of our approach. A high IoU value in medical imaging analysis reflects the model's ability to accurately delineate object boundaries. DeepLabV3+ represents a significant advancement in thyroid nodule segmentation, particularly for thyroid cancer screening and diagnosis. The obtained segmentation results suggest promising directions for future research, especially in the early detection of thyroid nodules. Deploying this algorithm on mobile devices offers a practical solution for early diagnosis and is likely to improve patient outcomes.

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.

Pre-treatment processing technology plays a crucial role in the overall freshwater fish processing procedure, and automatic head and tail cutting stands out as a significant pre-treatment technique within the industry. The system for removing the head and tail of freshwater fish comprised a Cartesian coordinate manipulator, a fish transfer device, a control system, and an image acquisition device. In the vision system, five image segmentation methods were utilized for fish head and tail image segmentation comparison tests. These methods include U-Net (U-shaped Deep Neural Network), DeeplabV3, PSPNet (Pyramid Scene Parsing Network), FastSCNN (Fast Semantic Segmentation Network), and ICNet (Image Cascade Network), all of which were employed to evaluate their performance. Among the tested segmentation methods, the ICNet demonstrated the most excellent segmentation capability. The experimental results indicated a segmentation accuracy of 99.01%, a mean intersection over union (MIoU) of 82.50%, and an image processing time of 15.25 ms. The results showed that the fish head and tail were successfully cut off using this model for recognition with a circular knife. Consequently, the segmentation model employed in the machine vision system within this study has demonstrated successful applicability in automatically cutting the heads and tails of freshwater fish of various sizes.

Forest fires pose a serious threat to the environment with the potential of causing ecological harm, financial losses, and human casualties. While research suggests that climate change will increase the frequency and severity of these fires, recent developments in deep learning and convolutional neural networks (CNN) have greatly enhanced fire detection techniques and capability. These models can be leveraged by unmanned aerial vehicles (UAVs) to automatically monitor burning areas. However, drones can carry only limited computational and power resources; therefore, on-board computing capabilities are constrained by hardware limitations. This work focuses on the design of segmentation models to identify and localize active burning areas from aerial RGB images processed on limited computing resources. To achieve this goal, the research compares the performance of different variants of the DeepLabv3 neural network model for fire segmentation when trained and tested with the FLAME dataset using a k-fold cross validation approach. Experimental results are compared with U-Net, a benchmark model used with the FLAME dataset, by implementing this model in the same codebase as the DeepLabv3 model. This work demonstrates that a refined version of DeepLabv3, with a MobileNetv2 backbone using pretrained layers and a simplified atrous spatial pyramid pooling (ASPP) module, yields a similar performance to U-Net, with a precision of 87.8% and a recall of 83.2%, while only requiring 20% of the number of parameters involved with the U-Net topology. This significantly reduces memory and power consumption, enabling longer UAV flight duration and reducing the processing overhead associated with sensor input, making it more suitable for deployment on unmanned aerial vehicles. The model’s compact architecture, implemented using TensorFlow and Keras for model design and training, along with OpenCV for image preprocessing, makes it portable and easy to integrate with edge devices such as NVIDIA Jetson boards.

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.

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.

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.

The fusion of spectral–polarimetric information can improve the autonomous reconnaissance capability of unmanned aerial vehicles (UAVs) in detecting artificial targets. However, the current spectral and polarization imaging systems typically suffer from low image sampling resolution, which can lead to the loss of target information. Most existing segmentation algorithms neglect the similarities and differences between multimodal features, resulting in reduced accuracy and robustness of the algorithms. To address these challenges, a real-time spectral–polarimetric segmentation algorithm for artificial targets based on an efficient attention fusion network, called ESPFNet (efficient spectral–polarimetric fusion network) is proposed. The network employs a coordination attention bimodal fusion (CABF) module and a complex atrous spatial pyramid pooling (CASPP) module to fuse and enhance low-level and high-level features at different scales from the spectral feature images and the polarization encoded images, effectively achieving the segmentation of artificial targets. Additionally, the introduction of the residual dense block (RDB) module refines feature extraction, further enhancing the network’s ability to classify pixels. In order to test the algorithm’s performance, a spectral–polarimetric image dataset of artificial targets, named SPIAO (spectral–polarimetric image of artificial objects) is constructed, which contains various camouflaged nets and camouflaged plates with different properties. The experimental results on the SPIAO dataset demonstrate that the proposed method accurately detects the artificial targets, achieving a mean intersection-over-union (MIoU) of 80.4%, a mean pixel accuracy (MPA) of 88.1%, and a detection rate of 27.5 frames per second, meeting the real-time requirement. The research has the potential to provide a new multimodal detection technique for enabling autonomous reconnaissance by UAVs in complex scenes.

Obtaining animal regions and the relative position relationship of animals in the scene is conducive to further studying animal habits, which is of great significance for smart animal farming. However, the complex breeding environment still makes detection difficult. To address the problems of poor target segmentation effects and the weak generalization ability of existing semantic segmentation models in complex scenes, a semantic segmentation model based on an improved DeepLabV3+ network (Imp-DeepLabV3+) was proposed. Firstly, the backbone network of the DeepLabV3+ model was replaced by MobileNetV2 to enhance the feature extraction capability of the model. Then, the layer-by-layer feature fusion method was adopted in the Decoder stage to integrate high-level semantic feature information with low-level high-resolution feature information at multi-scale to achieve more precise up-sampling operation. Finally, the SENet module was further introduced into the network to enhance information interaction after feature fusion and improve the segmentation precision of the model under complex datasets. The experimental results demonstrate that the Imp-DeepLabV3+ model achieved a high pixel accuracy (PA) of 99.4%, a mean pixel accuracy (MPA) of 98.1%, and a mean intersection over union (MIoU) of 96.8%. Compared to the original DeepLabV3+ model, the segmentation performance of the improved model significantly improved. Moreover, the overall segmentation performance of the Imp-DeepLabV3+ model surpassed that of other commonly used semantic segmentation models, such as Fully Convolutional Networks (FCNs), Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP), and U-Net. Therefore, this study can be applied to the field of scene segmentation and is conducive to further analyzing individual information and promoting the development of intelligent animal farming.

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.

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.

The coronavirus disease 2019 (COVID-19) epidemic has spread worldwide and the healthcare system is in crisis. Accurate, automated and rapid segmentation of COVID-19 lesion in computed tomography (CT) images can help doctors diagnose and provide prognostic information. However, the variety of lesions and small regions of early lesion complicate their segmentation. To solve these problems, we propose a new SAUNet++ model with squeeze excitation residual (SER) module and atrous spatial pyramid pooling (ASPP) module. The SER module can assign more weights to more important channels and mitigate the problem of gradient disappearance; the ASPP module can obtain context information by atrous convolution using various sampling rates. In addition, the generalized dice loss (GDL) can reduce the correlation between lesion size and dice loss, and is introduced to solve the problem of small regions segmentation of COVID-19 lesion. We collected multinational CT scan data from China, Italy and Russia and conducted extensive comparative and ablation studies. The experimental results demonstrated that our method outperforms state-of-the-art models and can effectively improve the accuracy of COVID-19 lesion segmentation on the dice similarity coefficient (our: 87.38 $$\%$$ vs. U-Net++: 84.25 $$\%$$ ), sensitivity (our: 93.28 $$\%$$ vs. U-Net++: 89.85 $$\%$$ ) and Hausdorff distance (our: 19.99 mm vs. U-Net++: 26.79 mm), respectively.

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.