The primary objective of this study is to develop an advanced, automated system for the early detection and classification of leaf diseases in potato plants, which are among the most cultivated vegetable crops worldwide. These diseases, notably early and late blight caused by Alternaria solani and Phytophthora infestans, significantly impact the quantity and quality of global potato production. We hypothesize that the integration of Vision Transformer (ViT) and ResNet-50 architectures in a new model, named EfficientRMT-Net, can effectively and accurately identify various potato leaf diseases. This approach aims to overcome the limitations of traditional methods, which are often labor-intensive, time-consuming, and prone to inaccuracies due to the unpredictability of disease presentation. EfficientRMT-Net leverages the CNN model for distinct feature extraction and employs depth-wise convolution (DWC) to reduce computational demands. A stage block structure is also incorporated to improve scalability and sensitive area detection, enhancing transferability across different datasets. The classification tasks are performed using a global average pooling layer and a fully connected layer. The model was trained, validated, and tested on custom datasets specifically curated for potato leaf disease detection. EfficientRMT-Net’s performance was compared with other deep learning and transfer learning techniques to establish its efficacy. Preliminary results show that EfficientRMT-Net achieves an accuracy of 97.65% on a general image dataset and 99.12% on a specialized Potato leaf image dataset, outperforming existing methods. The model demonstrates a high level of proficiency in correctly classifying and identifying potato leaf diseases, even in cases of distorted samples. The EfficientRMT-Net model provides an efficient and accurate solution for classifying potato plant leaf diseases, potentially enabling farmers to enhance crop yield while optimizing resource utilization. This study confirms our hypothesis, showcasing the effectiveness of combining ViT and ResNet-50 architectures in addressing complex agricultural challenges.

EfficientRMT-Net—An Efficient ResNet-50 and Vision Transformers Approach for Classifying Potato Plant Leaf Diseases

An algorithm based on the YOLOv5 model is proposed to address safety incidents such as tearing and blockage at transfer points on belt conveyors in coal mines caused by foreign objects mixed in with the coal flow. Given the tough underground conditions and images acquired with low quality, recursive filtering and MSRCR image enhancement algorithms were utilized to preprocess the dynamic images collected by underground monitoring devices, substantially enhancing image quality. The YOLOv5 model has been improved by introducing a multi-scale attention module (MSAM) during the channel map slicing, thereby increasing the model’s resistance to interference from redundant image features. Deep separable convolution was utilized in place of conventional convolution to detect, identify, and process large foreign objects on the belt conveyor as well as to increase detection speed. The MSAM-YOLOv5 model was trained before being installed on the NVIDIA Jetson Xavier NX platform and utilized to identify videos gathered from the coal mine belt conveyor. According to the experimental findings, the upgraded MSAM-YOLOv5 model has a greater recognition accuracy than YOLOv5L, with an average recall rate for different foreign objects of 96.27%, an average detection accuracy of 97.35%, and a recognition speed of 44 frames/s. The algorithm assures detection accuracy while increasing detection speed, satisfying the requirements for large foreign object detection on belt conveyors in coal mines. 

Detection of Large Foreign Objects on Coal Mine Belt Conveyor Based on Improved

Salient object detection (SOD) is a critical task in computer vision that involves accurately identifying and segmenting visually significant objects in an image. To address the challenges of gridding issues and feature dilution effects commonly encountered in SOD, we propose a sophisticated context-aware middle-layer guidance network (CMGNet). CMGNet incorporates the context-aware central-layer guidance module (CCGM), which utilizes cost-effective large kernels of depth-wise convolutions with embedded parallel channel attentions and squeeze-and-excitation (SeE) attentions mechanisms. It enables the model to effectively perceive objects of varying scales in complex scenarios. Additionally, the incorporation of the adjacent-to-central-layers paradigm enriches the model’s ability to capture more structural and contextual information. To further enhance performance, we introduce the dual-phase central-layer refinement module (DCRM), which effectively removes the minute blurry residuals in complex scenarios and enhances object segmentation. Moreover, we propose a novel hybrid loss function that handles hard pixels at or near boundaries by incorporating a weighting formula. This hybrid loss function combines binary cross-entropy (BCE), intersection over union (IoU), and consistency-enhanced loss (CEL), resulting in smoother and more precise saliency maps. Extensive evaluations on challenging datasets demonstrate the superiority of our approach over 15 state-of-the-art methods in salient object detection. 

CMGNet: Context-aware middle-layer guidance network for salient object detection

Parallel Adaptive Network for Polyp Segmentation

As an important computer vision technique, image segmentation has been widely used in various tasks. However, in some extreme cases, the insufficient illumination would result in a great impact on the performance of the model. So more and more fully supervised methods use multi-modal images as their input. The dense annotated large datasets are difficult to obtain, but the few-shot methods still can have satisfactory results with few pixel-annotated samples. Therefore, we propose the Visible-Depth-Thermal (three-modal) images few-shot semantic segmentation method. It utilizes the homogeneous information of three-modal images and the complementary information of different modal images, which can improve the performance of few-shot segmentation tasks. We constructed a novel indoor dataset VDT-2048-5i for the three-modal images few-shot semantic segmentation task. We also proposed a Self-Enhanced Mixed Attention Network (SEMANet), which consists of a Self-Enhanced module (SE) and a Mixed Attention module (MA). The SE module amplifies the difference between the different kinds of features and strengthens the weak connection for the foreground features. The MA module fuses the three-modal feature to obtain a better feature. Compared with the most advanced methods before, our model improves mIoU by 3.8% and 3.3% in 1-shot and 5-shot settings, respectively, which achieves state-of-the-art performance. In the future, we will solve failure cases by obtaining more discriminative and robust feature representations, and explore achieving high performance with fewer parameters and computational costs. 

Self-Enhanced Mixed Attention Network for Three-Modal Images Few-Shot Semantic Segmentation

Although deep learning-based techniques for salient object detection have considerably improved over recent years, estimated saliency maps still exhibit imprecise predictions owing to the internal complexity and indefinite boundaries of salient objects of varying sizes. Existing methods emphasize the design of an exemplary structure to integrate multi-level features by employing multi-scale features and attention modules to filter salient regions from cluttered scenarios. We propose a saliency detection network based on three novel contributions. First, we use a dense feature extraction unit (DFEU) by introducing large kernels of asymmetric and grouped-wise convolutions with channel reshuffling. The DFEU extracts semantically enriched features with large receptive fields and reduces the gridding problem and parameter sizes for subsequent operations. Second, we suggest a cross-feature integration unit (CFIU) that extracts semantically enriched features from their high resolutions using dense short connections and sub-samples the integrated information into different attentional branches based on the inputs received for each stage of the backbone. The embedded independent attentional branches can observe the importance of the sub-regions for a salient object. With the constraint-wise growth of the sub-attentional branches at various stages, the CFIU can efficiently avoid global and local feature dilution effects by extracting semantically enriched features via dense short-connections from high and low levels. Finally, a contour-aware saliency refinement unit (CSRU) was devised by blending the contour and contextual features in a progressive dense connected fashion to assist the model toward obtaining more accurate saliency maps with precise boundaries in complex and perplexing scenarios. Our proposed model was analyzed with ResNet-50 and VGG-16 and outperforms most contemporary techniques with fewer parameters.

/pdf/awanet-attentive-aware-wide-kernels-asymmetrical-network-1cvy29la.pdf

AWANet: Attentive-Aware Wide-Kernels Asymmetrical Network with Blended Contour Information for Salient Object Detection

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AWANet: Attentive-Aware Wide-Kernels Asymmetrical Network with Blended Contour Information for Salient Object Detection