Edge-guided Representation Learning for Underwater Object Detection

Underwater object detection (UOD) is crucial for marine economic development, environmental protection, and the planet's sustainable development. The main challenges of this task arise from low-contrast, small objects, and mimicry of aquatic organisms. The key to addressing these challenges is to focus the model on obtaining more discriminative information. We observe that the edges of underwater objects are highly unique and can be distinguished from low-contrast or mimicry environments based on their edges. Motivated by this observation, we propose an Edge-guided Representation Learning Network, termed ERL-Net, that aims to achieve discriminative representation learning and aggregation under the guidance of edge cues. Firstly, we introduce an edge-guided attention module to model the explicit boundary information, which generates more discriminative features. Secondly, a feature aggregation module is proposed to aggregate the multi-scale discriminative features by regrouping them into three levels, effectively aggregating global and local information for locating and recognizing underwater objects. Finally, we propose a wide and asymmetric receptive field block to enable features to have a wider receptive field, allowing the model to focus on more small object information. Comprehensive experiments on three challenging underwater datasets show that our method achieves superior performance on the UOD task

Reduced attentional inhibition for peripheral distractors of angry faces under central perceptual load in deaf individuals: evidence from an event-related potentials study

BackgroundStudies have shown that deaf individuals distribute more attention to the peripheral visual field and exhibit enhanced visual processing for peripheral stimuli relative to hearing individuals. This leads to better detection of peripheral target motion and simple static stimuli in hearing individuals. However, when threatening faces that represent dangerous signals appear as non-targets in the periphery, it remains unclear whether deaf individuals would retain an advantage over hearing individuals in detecting them.MethodsIn this study, 23 deaf and 28 hearing college students were included. A modified perceptual load paradigm and event-related potentials (ERPs) were adopted. In the task, participants were instructed to search for a target letter in a central letter array, while task-irrelevant face distractors (happy, neutral, and angry faces) were simultaneously presented in the periphery while the central perceptual load was manipulated.ResultsBehavioral data showed that angry faces slowed deaf participants' responses to the target while facilitating the responses of hearing participants. At the electrophysiological level, we found modulation of P1 amplitude by central load only in hearing individuals. Interestingly, larger interference from angry face distractors was associated with higher P1 differential amplitude only in deaf individuals. Additionally, the amplitude of N170 for happy face distractors was smaller than that for angry and neutral face distractors in deaf participants.ConclusionThe present data demonstrates that, despite being under central perceptual load, deaf individuals exhibit less attentional inhibition to peripheral, goal-irrelevant angry faces than hearing individuals. The result may reflect a compensatory mechanism in which, in the absence of auditory alertness to danger, the detection of visually threatening information outside of the current attentional focus has a high priority

The role of smart polymeric biomaterials in bone regeneration: a review

Addressing critical bone defects necessitates innovative solutions beyond traditional methods, which are constrained by issues such as immune rejection and donor scarcity. Smart polymeric biomaterials that respond to external stimuli have emerged as a promising alternative, fostering endogenous bone regeneration. Light-responsive polymers, employed in 3D-printed scaffolds and photothermal therapies, enhance antibacterial efficiency and bone repair. Thermo-responsive biomaterials show promise in controlled bioactive agent release, stimulating osteocyte differentiation and bone regeneration. Further, the integration of conductive elements into polymers improves electrical signal transmission, influencing cellular behavior positively. Innovations include advanced 3D-printed poly (l-lactic acid) scaffolds, polyurethane foam scaffolds promoting cell differentiation, and responsive polymeric biomaterials for osteogenic and antibacterial drug delivery. Other developments focus on enzyme-responsive and redox-responsive polymers, which offer potential for bone regeneration and combat infection. Biomaterials responsive to mechanical, magnetic, and acoustic stimuli also show potential in bone regeneration, including mechanically-responsive polymers, magnetic-responsive biomaterials with superparamagnetic iron oxide nanoparticles, and acoustic-responsive biomaterials. In conclusion, smart biopolymers are reshaping scaffold design and bone regeneration strategies. However, understanding their advantages and limitations is vital, indicating the need for continued exploratory research

AO2-DETR: Arbitrary-Oriented Object Detection Transformer

Arbitrary-oriented object detection (AOOD) is a challenging task to detect objects in the wild with arbitrary orientations and cluttered arrangements. Existing approaches are mainly based on anchor-based boxes or dense points, which rely on complicated hand-designed processing steps and inductive bias, such as anchor generation, transformation, and non-maximum suppression reasoning. Recently, the emerging transformer-based approaches view object detection as a direct set prediction problem that effectively removes the need for hand-designed components and inductive biases. In this paper, we propose an Arbitrary-Oriented Object DEtection TRansformer framework, termed AO2-DETR, which comprises three dedicated components. More precisely, an oriented proposal generation mechanism is proposed to explicitly generate oriented proposals, which provides better positional priors for pooling features to modulate the cross-attention in the transformer decoder. An adaptive oriented proposal refinement module is introduced to extract rotation-invariant region features and eliminate the misalignment between region features and objects. And a rotation-aware set matching loss is used to ensure the one-to-one matching process for direct set prediction without duplicate predictions. Our method considerably simplifies the overall pipeline and presents a new AOOD paradigm. Comprehensive experiments on several challenging datasets show that our method achieves superior performance on the AOOD task

Research on Short Video Hotspot Classification Based on LDA Feature Fusion and Improved BiLSTM

Short video hot spot classification is a fundamental method to grasp the focus of consumers and improve the effectiveness of video marketing. The limitations of traditional short text classification are sparse content as well as inconspicuous feature extraction. To solve the problems above, this paper proposes a short video hot spot classification model combining latent dirichlet allocation (LDA) feature fusion and improved bi-directional long short-term memory (BiLSTM), namely the LDA-BiLSTM-self-attention (LBSA) model, to carry out the study of hot spot classification that targets Carya cathayensis walnut short video review data under the TikTok platform. Firstly, the LDA topic model was used to expand the topic features of the Word2Vec word vector, which was then fused and input into the BiLSTM model to learn the text features. Afterwards, the self-attention mechanism was employed to endow different weights to the output information of BiLSTM in accordance with the importance, to enhance the precision of feature extraction and complete the hot spot classification of review data. Experimental results show that the precision of the proposed LBSA model reached 91.52%, which is significantly improved compared with the traditional model in terms of precision and F1 value

WT-YOLOM: An Improved Target Detection Model Based on YOLOv4 for Endogenous Impurity in Walnuts

Since impurities produced during walnut processing can cause serious harm to human health, strict quality control must be carried out during production. However, most detection equipment still uses photoelectric detection technology to automatically sort heterochromatic particles, which is unsuitable for detecting endogenous foreign bodies with similar colors. Therefore, this paper proposes an improved YOLOv4 deep learning object detection algorithm, WT-YOLOM, for detecting endogenous impurities in walnuts—namely, oily kernels, black spot kernels, withered kernels, and ground nutshells. In the backbone of the model, a lightweight MobileNet module was used as the encoder for the extraction of features. The spatial pyramid pooling (SPP) structure was improved to spatial pyramid pooling—fast (SPPF), and the model size was further reduced. Loss function was replaced in this model with a more comprehensive SIoU loss. In addition, efficient channel attention (ECA) mechanisms were applied after the backbone feature map to improve the model’s recognition accuracy. This paper compares the recognition speed and accuracy of the WT-YOLOM algorithm with the Faster R-CNN, EfficientDet, CenterNet, and YOLOv4 algorithms. The results showed that the average precision of this model for different kinds of endogenous impurities in walnuts reached 94.4%. Compared with the original model, the size was reduced by 88.6%, and the recognition speed reached 60.1 FPS, which was an increase of 29.0%. The metrics of the WT-YOLOM model were significantly better than those of comparative models and can significantly improve the detection efficiency of endogenous foreign bodies in walnuts

Edge-guided representation learning for underwater object detection

Underwater object detection (UOD) is crucial for marine economic development, environmental protection, and the planet's sustainable development. The main challenges of this task arise from low-contrast, small objects, and mimicry of aquatic organisms. The key to addressing these challenges is to focus the model on obtaining more discriminative information. The authors observe that the edges of underwater objects are highly unique and can be distinguished from low-contrast or mimicry environments based on their edges. Motivated by this observation, an Edge-guided Representation Learning Network, termed ERL-Net is proposed, that aims to achieve discriminative representation learning and aggregation under the guidance of edge cues. Firstly, an edge-guided attention module is introduced to model the explicit boundary information, which generates more discriminative features. Secondly, a hierarchical feature aggregation module is proposed to aggregate the multi-scale discriminative features by regrouping them into three levels, effectively aggregating global and local information for locating and recognising underwater objects. Finally, a wide and asymmetric receptive field block is proposed to enable features to have a wider receptive field, allowing the model to focus on smaller object information. Comprehensive experiments on three challenging underwater datasets show that our method achieves superior performance on the UOD task.ISSN:2468-232