Seed germination rate is a critical factor in agricultural productivity. Traditional approaches to germination assessment necessitate human scrutiny, introducing subjectivity and diminishing operational efficiency. While fully supervised deep learning approaches offer objectivity, reproducibility and efficiency, they require large-scale and high-quality labeled datasets, which are often challenging to obtain. To address this limitation, this study introduce a Semi-Supervised Germination Detection (SSGD) method built upon the Soft Teacher framework. SSGD employs a Faster R-CNN detector with ResNet50-FPN feature extraction network in both the teacher model and the student model. To ensure higher learning stability, the parameters of the teacher model are dynamically improved by an exponential moving average (EMA) updating mechanism. This study conducted a comprehensive evaluation of SSGD on the publicly available Pennisetum glaucum (PG), Secale cereale (SC), and Zea mays (ZM) datasets. Remarkably, with only 10% of labeled data, SSGD achieved mAP50 scores of 0.954, 0.928, and 0.961 on PG, SC, and ZM, respectively, surpassing fully supervised methods trained on 100% labeled data, including YOLOv3, FCOS, Cascade R-CNN and Faster R-CNN. Moreover, SSGD consistently outperformed the Faster R-CNN baseline across various annotation ratios (1%, 10%, 20% and 30%). Notably, even when the PG dataset’s labeling rate dropped to only 1%, SSGD maintained a high mAP50 of 0.94, exceeding the baseline by 5 percentage points. These findings underscore SSGD’s strong adaptability to limited data and further emphasize the effectiveness of semi-supervised learning in seed germination detection.

The integration of hyperspectral imaging (HSI) and computer vision (CV) technologies enables rapid, accurate, and non-destructive seed quality detection methods, providing technical support for accurate seed classification and rational utilization of seed resources. However, in practical applications, challenges such as high computational costs and complex feature extraction remain, leading to limited modeling capability. This paper summarizes applications of CV-based HSI technology in seed quality detection, focusing on the current research status of seed quality-related models and methods, such as 1) variety classification; 2) vigor assessment; 3) moisture content determination; 4) health determination; along with the future development trends.

With the rise of autonomous shipping and intelligent maritime surveillance, accurately detecting ships under multiscale dense occlusions has become a critical challenge. Traditional detectors struggle with feature degradation caused by overlapping targets and scale variation. To address this, we propose DSONet, a lightweight framework tailored for robust ship detection in complex maritime environments. Specifically, we design a task-specific feature extraction structure, DualFeatureDetection (DF-Det), which enhances spatial detail preservation while reducing redundant computation. Additionally, we introduce the AdaptiveUpsample (AU) module to improve multiscale feature fusion and spatial reconstruction, especially under occlusion. Integrated with a four-branch oriented bounding box (OBB) detection head, DSONet achieves precise localization of elongated and overlapping ships. Extensive experiments on the MID, SeaShip and SSDD datasets demonstrate that DSONet outperforms existing detectors in both accuracy and efficiency, offering a practical solution for maritime occlusion detection.

Area and reliability optimization have become the primary constraints in circuits logic synthesis. To address the increasing area and transient fault susceptibility in combinational circuits, we propose a high-dimensional genetic algorithm (HGA). HGA adopts an evolutionary scheme based on ternary tree, and uses adaptive crossover operator and flight operator to jump out of local optimum. Moreover, based on the HGA, we propose an area and reliability optimization method (AROM) for mixed polarity Reed-Muller logic circuits, which searches the best polarity with minimum area and soft error rate. The experimental results confirm that AROM can search for more desirable nondominated solutions in less time compared to existing optimization methods, and can be used as an effective electronic design automation tool for multi-objective optimization.

Ship detection in remote sensing imagery is crucial for various maritime applications such as surveillance and navigation. Convolutional neural networks (CNNs) and transformers have shown significant potential in object detection within the field of image processing. However, existing models applied directly to ship detection in synthetic aperture radar (SAR) imagery encounter challenges due to the varying sizes of ship targets. This often leads to issues such as low detection accuracy, missed detections, and false alarms. In this letter, we propose a new detection network, HMA-Net, to further address these issues. Initially, we introduce the Cwin module, which enhances interference resistance at a relatively low cost, enabling the model to more accurately capture target information. Subsequently, we design a multiscale ship feature extraction module, which uses a parallel multibranch structure to extract features of ships of various sizes and shapes. Finally, we introduce an adaptive fusion loss function that flexibly allocates loss calculation methods to detected targets, thereby enhancing the robustness of the model and achieving high-quality detection boxes. The proposed HMA-Net achieved improvements of 2.0% and 0.9% in mAP.50:.95 over the baseline models on the SAR Ship Detection dataset and the High-Resolution SAR Images dataset, using only 3.52 M parameters.

Detecting dense small objects (DSOs) of varying scales still remains a challenging research problem in remote sensing imagery (RSI). Due to their weak feature extraction capabilities for small objects, most existing detection approaches struggle to handle the high proportion of DSO in RSI, thereby increasing the likelihood of missed detections. In addition, the close proximity and overlap of multiscale objects further complicate detection due to occlusion between bounding boxes. In this study, we systematically propose a novel loss function, remote sensing dense small target detection (RSDS), for detecting DSO in RSI, which contains three main components. The first is Gaussian reassignment loss (GRL), which adaptively redistributes sample weights to prevent any single sample (such as positive, negative, easy, and hard samples) from dominating the overall loss. To solve the zero-loss issue in traditional intersection over union (IoU) and intersection over ground truth (IoG) metrics when object boxes do not intersect, we design the Gaussian Wasserstein distance (WD) penalty loss, which models the eligible 2-D detection boxes as Gaussian distributions and calculates the similarity between them. The final one, which we called the occlusion box interaction loss, explains the attraction between DSO and the repulsion from their surroundings. Deploying RSDS not only significantly reduces the probability of missing DSO in RSI, but also enhances the detection accuracy in other similar computer vision tasks. Experiments on the HRSID, NWPU-10, and SSDD datasets show that some general models incorporating RSDS achieve precision improvements of 6.68%, 11.52%, and 5.26%, and 8.71%, 5.17%, and 9.34% in$\text {mAP}_{0.5}$and$\text {mAP}_{0.5\text {:}0.95}$, respectively, compared with other baselines. The code will be found athttps://github.com/CCC0090/RDSD-Loss.

In the field of Earth observation and computer vision, oriented object detection for remotesensing images is a crucial task that aims to locate objects more accurately in complex scenes containing a large number of densely arranged, large aspect ratio, and arbitrarily oriented objects. Although recently proposed methods have achieved remarkable performance, there are still several challenges to address: 1) interference from complex backgrounds, 2) imbalanced and mismatched label assignments caused by tiny objects and objects with large aspect ratios, and 3) misalignment between the tasks of classification and localization. In this article, we propose a frequency-assisted dynamic learning network (FADL-Net) to overcome the crucial challenges. Concretely, we introduce a spatial-spectral feature pyramid network to adaptively capture global long-range dependency feature representations containing various frequency domains. Meanwhile, to produce more reliable training samples for objects with extreme shapes, we design a geometric aware dynamic label assignment to dynamically mitigate the imbalance and mismatch in label assignment in a coarse-to-fine manner, thereby achieving more stable optimization during the training process. Moreover, we propose a joint-learning rotated quality loss that addresses the inconsistency between classification and localization by dynamically adapting the weights of different samples in the training stage. Extensive experiments on several public remote sensing datasets demonstrate that our method performs favorably against state-of-the-art detection approaches.

Semisupervised object detection (SSOD) has garnered significant interest for its capability to enhance the detection performance by leveraging large amounts of unlabeled data. However, current SSOD methods primarily focus on detecting horizontal objects, with little research devoted to the detection of arbitrary-oriented objects in remote sensing images. Drawing inspiration from this limitation, this article proposes a semisupervised oriented object detection framework (S$^{2}$O-Det) to reduce annotation costs while improving detection performance in a semisupervised manner. Initially, the proposed task-consistent learning aims to alleviate the inconsistencies between classification and localization, which provides consistent confidence for the pseudolabels. Subsequently, the introduced coarse-to-fine sample mining employs dense prediction for pseudolabel assignment, adopting a divide-and-conquer approach to independently identify consistent and reliable labels for both classification and localization tasks. Finally, a probabilistic distillation loss ensures the harmonization of the probability distributions across the teacher and student feature domains, thereby reciprocally enhancing the learning competencies. Experimental results on the DOTA-v1.0 and DOTA-v1.5 datasets demonstrate that S$^{2}$O-Det achieves promising performance across different labeling ratios.

In recent years, transformer-based networks, known for their ability to model long-range dependencies, have been widely used in downstream computer vision tasks, surpassing certain neural network architectures. However, transformer-based networks suffer from issues such as large parameter size, high computational complexity, and difficulties in extending spatial and channel features to the third or even higher orders, resulting in convergence challenges for small to medium-sized datasets and limited effectiveness in extracting high-order detailed features. In this paper, we propose a high-order spatial and channel controllable convolution module, named CSn, which can replace standard convolutions in any convolutional network. In the context of remote sensing small object detection, CSndemonstrates superior performance compared to Self-Attention structures embedded in neural networks. Moreover, it introduces long-range dependency relationships among pixels, similar to Self-Attention, and adopts a cascaded recursive approach to extend spatial and channel features to arbitrary higher orders without introducing significant additional computation. This extension captures crucial information from high-order spatial and channel dimensions, resulting in improved accuracy for small object detection. Additionally, we construct a novel, versatile CSn-(FPN+PAN) structure for object detection networks, referred to as CSnNet. Finally, our proposed model exhibits significant advantages in remote sensing detection when compared to state-of-the-art methods on publicly available SAR datasets (SSDD, HRSID) and optical remote sensing dataset (NWPU-10), achieving respective improvements of 3.4%, 4.5%, and 0.4% in mAP50compared to baseline models.