Complex Natural Environments Research Articles

CECS-CLIP: Fusing Domain Knowledge for Rare Wildlife Detection Model.

Accurate and efficient wildlife monitoring is essential for conservation efforts. Traditional image-based methods often struggle to detect small, occluded, or camouflaged animals due to the challenges posed by complex natural environments. To overcome these limitations, an innovative multimodal target detection framework is proposed in this study, which integrates textual information from an animal knowledge base as supplementary features to enhance detection performance. First, a concept enhancement module was developed, employing a cross-attention mechanism to fuse features based on the correlation between textual and image features, thereby obtaining enhanced image features. Secondly, a feature normalization module was developed, amplifying cosine similarity and introducing learnable parameters to continuously weight and transform image features, further enhancing their expressive power in the feature space. Rigorous experimental validation on a specialized dataset provided by the research team at Northwest A&F University demonstrates that our multimodal model achieved a 0.3% improvement in precision over single-modal methods. Compared to existing multimodal target detection algorithms, this model achieved at least a 25% improvement in AP and excelled in detecting small targets of certain species, significantly surpassing existing multimodal target detection model benchmarks. This study offers a multimodal target detection model integrating textual and image information for the conservation of rare and endangered wildlife, providing strong evidence and new perspectives for research in this field.

IC-IE-AKS-O: an automatic recognition method for coastal slope landslide areas

Automatically and accurately identifying the deformation zone of coastal slope landslides is crucial for exploring the mechanism of landslides and predicting landslide disasters. To this end, this study proposes an integrated automatic recognition method combining Image Clipping (IC), Image Information Enhancement (IE), Adaptive K-means Clustering Segmentation (AKS), and Optimization (O): IC-IE-AKS-O, which achieves precise extraction of the deformation area in coastal slope landslide images. Firstly, due to the more complex natural environment of field slopes, to extend the monitoring duration, we introduce a hierarchical operation algorithm based on the HSV color model, which effectively mitigates the impact of sunlight, rain, and foggy weather on image recognition accuracy. Secondly, this study proposes a 2D landslide image segmentation technique that combines K-means clustering with global threshold segmentation for landslide images, enabling the segmentation of small image regions with precision. Finally, we combine image information enhancement technology with image segmentation technology. To verify its effectiveness, we identify a landslide image of a coastal slope in Pingtan. The method displays an average relative error of 5.20% and 5.14% in the X and Y directions, respectively. Its advantages are threefold: (1) The combination of image information enhancement and segmentation techniques can more accurately identify landslide areas that appear blurred in the image; (2) expanding the temporal dimension of coastal slope monitoring; (3) providing excellent boundary conditions and segmentation results. The practical application of this method ensures the stable and accurate operation of the coastal slope monitoring system, providing a safeguard for the sustainable development of marine safety.

Incremental RPN: Hierarchical Region Proposal Network for Apple Leaf Disease Detection in Natural Environments.

Apple leaf diseases can seriously affect apple production and quality, and accurately detecting them can improve the efficiency of disease monitoring. Owing to the complex natural growth environment, apple leaf lesions may be easily confused with background noise, leading to poor performance. In this study, a cascaded Incremental Region Proposal Network (Inc-RPN) is proposed to accurately detect apple leaf diseases in natural environments. The proposed Inc-RPN has a two-layer RPN architecture, where the precursor RPN is leveraged to generate diseased leaf proposals, and the successor RPN focuses on extracting target disease spots based on diseased leaf proposals. In the successor RPN, a low-level feature aggregation module is designed to fully utilize the bridged features and preserve the semantic information of the target disease spots. An incremental module is also leveraged to extract aggregated diseased leaf features and target disease spot features. Finally, a novel position anchor generator is designed to generate anchors based on diseased leaf proposals. The experimental results show that the proposed Inc-RPN performs very well on the FALD_CED and Apple Leaf Disease datasets, showing that it can accurately perform apple leaf disease detection tasks.

Distribution and diversity of ticks determined by environmental factors in Ningxia, China

Ticks are important vectors of zoonotic pathogens, and represent an increasing threat for human and animal health. Considering the complex natural environments of Ningxia Hui Autonomous Region, China, we expect the diverse tick species in this region. Here, we conduct a field survey on parasitic and host-seeking ticks. A total of 10,419 ticks were collected, which belonged to nine species of four genera. There were significant differences in terms of vegetation index, altitude, and seven climatic factors among the four tick genera —Hyalomma, Dermacentor, Haemaphysalis, and Ixodes, except between Haemaphysalis and Ixodes, where no significant differences were observed in these factors. The ecological niche modelling revealed that the suitable habitats for Hyalomma asiaticum was in the northwest Ningxia, with annual ground surface temperature as the most important factor. The suitable area for Dermacentor nuttalli was in the southwest and eastern regions of Ningxia with elevation as the highest contribution. D. silvarum was best suited to the southern Ningxia also with elevation as the most important factor. The four tick species including Haemaphysalis longicornis, Hae. qinghaiensis, Hae. japonica, and Ixodes persulcatus were best suited to the southernmost Ningxia with annual precipitation as the main factors for Hae. longicornis and elevation for the other three ticks. The results of predicted potential distribution of different tick species provide a scientific basis for the prevention and control of ticks and tick-borne diseases in the region. Furthermore, the subsequent impacts of the Greening Program to regain forests and grasslands from former agricultural lands in Ningxia on tick population dynamics deserve further investigation.

Classification of infection grade for anthracnose in mango leaves under complex background based on CBAM-DBIRNet

Anthracnose is one of the most harmful fungal diseases in mango orchards. Accurate and rapid detection of anthracnose infection grade for mango leaves in complex natural environments is a prerequisite of precise variable pesticide spraying by robots in intelligent orchards. In this paper, A lightweight double branch inverted residual attention network (CBAM-DBIRNet) model was proposed by combining the inverted residual network and the attention mechanism, which was used to detect the infection grade of anthracnose for mango leaves in natural environment. The model designed two parallel residual branch structures to extract multi-scale features of mango leaves with different infection grades through different dilation coefficients. The inverted residual structure and depthwise separable convolution were used to reduce the complexity of the model. The CBAM attention module was integrated into the model to suppress the interference of complex background information. The mango leaves collected in the natural orchard were used for experiments. The results showed that the accuracy, recall and F1 of the CBAM-DBIRNet model for the classification of anthracnose infection grade were 98.42 %, 97.80 % and 97.89 %, respectively. The model size and parameter were 0.64 MB and 0.15 M, respectively. This study provides technical support for smart orchard management and robotic precision variable application operations.

V-YOLO: A Lightweight and Efficient Detection Model for Guava in Complex Orchard Environments

The global agriculture industry is encountering challenges due to labor shortages and the demand for increased efficiency. Currently, fruit yield estimation in guava orchards primarily depends on manual counting. Machine vision is an essential technology for enabling automatic yield estimation in guava production. To address the detection of guava in complex natural environments, this paper proposes an improved lightweight and efficient detection model, V-YOLO (VanillaNet-YOLO). By utilizing the more lightweight and efficient VanillaNet as the backbone network and modifying the head part of the model, we enhance detection accuracy, reduce the number of model parameters, and improve detection speed. Experimental results demonstrate that V-YOLO and YOLOv10n achieve the same mean average precision (mAP) of 95.0%, but V-YOLO uses only 43.2% of the parameters required by YOLOv10n, performs calculations at 41.4% of the computational cost, and exhibits a detection speed that is 2.67 times that of YOLOv10n. These findings indicate that V-YOLO can be employed for rapid detection and counting of guava, providing an effective method for visually estimating fruit yield in guava orchards.

Temporal characteristics of turbulent flow and moth orientation behaviour patterns with fluent simulation and moth-based moving model simulation

ObjectivePrevious research has explored the pheromone release patterns of female moths, revealing species-specific release frequencies and the transmission of temporal information through odourant plumes in turbulent flows. Varying the release frequency during the orientation process results in distinct orientation behaviours. Studies on moth movement patterns have determined that encounters and deviations from odour plumes elicit distinct reactions; the time interval between each movement pattern is measured as the “reaction time,” and the interval between each detection and loss of odourant plume is measured as the “gap length.” MethodsWe simulated turbulent flow at various release frequencies. Our efforts focused on establishing a model that could simulate the joint orientation movement under turbulent flow and intermittent plumes. We built an agent moving mechanism, including wind velocity information, with particular reference to the temporal parameter and orientation success efficiency. ResultsWe calculated the time threshold of each burst in different simulations under different wind velocities and release frequencies. The time structure characteristics of the plume along the turbulent flow vary depending on the distance from the source. We simulated walking agents in a turbulent environment and recorded their behaviour processes. The reaction time, release interval, and time threshold were related to the orientation results. ConclusionOn the basis of previous experimental results and our simulations, we conclude that the designated interval time likely enhances search efficiency. The complex and dynamic natural environment presents various opportunities for using this unique odour-source searching capability in different scenarios, potentially improving the control systems of odour-searching robots.

In-stream attenuation and enantioselective fractionation of psychiatric pharmaceuticals in a wastewater effluent-dominated river basin

Wastewater effluent is the main contributor of psychiatric pharmaceuticals (PPs) pollution in surface waters. However, little is known about its spatial evolution dynamics in effluent-dominated rivers. Herein, 10 representative PPs, including 6 chiral pharmaceuticals and 4 achiral pharmaceuticals, were explored in the Beiyun River, a typical wastewater effluent-dominated river, to explore their occurrence, in-stream attenuation and enantioselective fractionation behaviors at a watershed scale. Among the target substances, 8 and 9 drugs were detected in surface water and sediment samples with the ΣPPs concentrations ranging from 78.4 to 260.1 ng/L and 4.8 to 43.4 ng/g dw in surface water and sediments, respectively. Along the mainstream of the Beiyun River, only several PPs detected in surface water, e.g., citalopram, O-demethylvenlafaxine, and fluoxetine, exhibited in-stream attenuation behaviors when reaching rural area, while all PPs detected in sediments displayed in-stream attenuation behavior. Four chiral PPs detected in surface water exhibited an enantioselective attenuation phenomenon, while in sediments, only citalopram displayed an enantioselective fractionation behavior. The differences in the in-stream attenuation and enantioselective environmental behavior of individual PPs caused complex contaminant evolution along the stream reach. This work provides enantiomeric profiles of chiral pollutants for evaluating their in-stream attenuation processes, which would facilitate better understanding of the changing contaminant exposure conditions in complex natural environments.

A novel image dehazing algorithm for complex natural environments

As an essential preprocessing step in computer vision tasks, image dehazing is preparatory work in many practical applications. To ensure the navigation safety of intelligent ships in complex haze weather, this paper proposes a lightweight real-time dehazing method for the natural environment based on an all-in-one dehazing network. Specifically, a hybrid dilated convolution is used to construct a dehazing network, which effectively expands the receptive field and improves the ability to extract characteristic information without increasing the calculation amount of the model. Secondly, a hybrid attention module is developed to enhance the model's ability to remove haze while retaining more detailed features by giving weights to light haze and heavy haze areas in the image. Finally, considering the subjective perception of human eyes, a mixed loss function is designed to address image dimness and color distortion after dehazing. Through experiments on both synthetic and real-world haze datasets, the proposed method attains ideal dehazing results and has high real-time performance. It can dehaze under different haze conditions, address the critical challenge of degraded visual perception in severe weather conditions of intelligent ship navigation on inland rivers in complex environments, and promote intelligent ships' development.

COTTON-YOLO: Enhancing Cotton Boll Detection and Counting in Complex Environmental Conditions Using an Advanced YOLO Model

This study aims to enhance the detection accuracy and efficiency of cotton bolls in complex natural environments. Addressing the limitations of traditional methods, we developed an automated detection system based on computer vision, designed to optimize performance under variable lighting and weather conditions. We introduced COTTON-YOLO, an improved model based on YOLOv8n, incorporating specific algorithmic optimizations and data augmentation techniques. Key innovations include the C2F-CBAM module to boost feature recognition capabilities, the Gold-YOLO neck structure for enhanced information flow and feature integration, and the WIoU loss function to improve bounding box precision. These advancements significantly enhance the model’s environmental adaptability and detection precision. Comparative experiments with the baseline YOLOv8 model demonstrated substantial performance improvements with COTTON-YOLO, particularly a 10.3% increase in the AP50 metric, validating its superiority in accuracy. Additionally, COTTON-YOLO showed efficient real-time processing capabilities and a low false detection rate in field tests. The model’s performance in static and dynamic counting scenarios was assessed, showing high accuracy in static cotton boll counting and effective tracking of cotton bolls in video sequences using the ByteTrack algorithm, maintaining low false detections and ID switch rates even in complex backgrounds.

Looming Detection in Complex Dynamic Visual Scenes by Interneuronal Coordination of Motion and Feature Pathways

Detecting looming signals for collision avoidance encounters challenges in real‐world scenarios, where moving backgrounds can interfere as an agent navigates through complex natural environments. Remarkably, even insects with limited neural systems adeptly respond to looming stimuli while in motion at high speeds. Existing insect‐inspired looming detection models typically rely on either motion‐pathway or feature‐pathway signals, yet both are susceptible to dynamic visual scene interference. Coordinating interneuron signals from both pathways can enhance the looming detection performance under dynamic conditions. An artificial neural network is employed to construct a combined‐pathway model based on Drosophila anatomy. The model outperforms state‐of‐the‐art bio‐inspired looming‐detection models in tasks involving dynamic backgrounds, simulated by animated 2D‐moving natural scenes or recorded in reality when an unmanned aerial vehicle performs obstacle collision avoidance tasks. Notably, by combining neural anatomy architecture and appropriate multiobjective tasks, the model exhibits convergent neural dynamics with biological counterparts post‐training, offering network explanations and mechanistic insights. Specifically, a multiplicative interneuron operation enhances looming signal patterns and reduces background interferences, generalizing to more complex scenarios, such as AirSim 3D environments and real‐world situations. The work introduces testable biological hypotheses and a promising bioinspired solution for looming detection in dynamic visual environments.

Pest-ConFormer: A hybrid CNN-Transformer architecture for large-scale multi-class crop pest recognition

Crop pests are acknowledged as the major factors in reducing the yield and quality of agricultural production worldwide. It is an urgent necessity to recognize crop pests accurately to protect the crop in the early stage to reduce the loss for the agricultural economy. Due to the ecological characteristics of the crop pests and the complex background in fields, the crop pests show high inter-class similarity and significant intra-class variation in external morphology appearance, which makes current recognition methods suffer from low classification accuracy and poor generalization ability in complex natural environment recognition tasks. To tackle this problem, a hybrid convolutional neural network and transformer-based model, namely Pest-ConFormer, featured with multi-scale weakly supervised feature selection mechanisms is proposed, which has shown excellent multi-scale discriminative feature extraction in fine-grained visual classification (FGVC) tasks. This method employs a hybrid convolution-transformer encoder architecture pre-training in a self-supervised masked autoencoder manner as a backbone to learn pests’ highly discriminative features across various scales. Next, a dual-path feature aggregation structure with a top-down FPN-like feature pathway and a bottom-up PANet-like feature pathway based on attention mechanisms is designed to learn high-level global context information and low-level local detailed feature representation. Thirdly, a fine-grained classification module using weakly supervised learning is introduced to select the discriminative feature points in different pyramidal levels. Then, these feature points are fed into a graph convolutional network to accomplish classification. Several experiments are conducted on the large-scale multi-class IP102 benchmark dataset, and the proposed method achieves an accuracy of 77.81 % regarding crop pest recognition. The experimental results indicate that our approach outperforms other state-of-the-art methods by nearly 2 percent points, demonstrating that the proposed hybrid architecture with dual-path feature aggregation and fine-grained classification modules can be more effective in the crop pest recognition field than CNN-based methods and can be deployed in the practical natural environment. The source code will be available at https://github.com/mwfang/pestconformer.

Interactions between Iron Minerals and Dissolved Organic Matter Derived from Microplastics Inhibited the Ferrihydrite Transformation as Revealed at the Molecular Scale.

Microplastic-derived dissolved organic matter (MP-DOM) is an emerging carbon source in the environment. Interactions between MP-DOM and iron minerals alter the transformation of ferrihydrite (Fh) as well as the distribution and fate of MP-DOM. However, these interactions and their effects on both two components are not fully elucidated. In this study, we selected three types of MP-DOM as model substances and utilized Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) and extended X-ray absorption fine structure (EXAFS) spectroscopy to characterize the structural features of DOMs and DOM-mineral complexes at the molecular and atomic levels. Our results suggest that carboxyl and hydroxyl groups in MP-DOM increased the Fe-O bond length by 0.02-0.03 Å through interacting with Fe atoms in the first shell, thereby inhibiting the transformation of Fh to hematite (Hm). The most significant inhibition of Fh transformation was found in PS-DOM, followed by PBAT-DOM and PE-DOM. MP-DOM components, such as phenolic compounds and condensed polycyclic aromatics (MW > 360 Da) with high oxygen content and high unsaturation, exhibited stronger mineral adsorption affinity. These findings provide a profound theoretical basis for accurately predicting the behavior and fate of iron minerals as well as MP-DOM in complex natural environments.

YOLO Adaptive Developments in Complex Natural Environments for Tiny Object Detection

YOLO Adaptive Developments in Complex Natural Environments for Tiny Object Detection

Study on mechanical properties and molecular simulation of nano‐SiO2‐modified hybrid fiber reinforced polymer bar under the dry and alkaline environment

AbstractFiber reinforced polymer (FRP) bars have received extensive promotion and application in the structural engineering. However, due to the limitations inherent in using single material, FRP reinforced concrete structures often encounter issues, such as brittle failure and inadequate energy dissipation, particularly in complex natural environments and under various loading conditions. In this study, the preparation of nano‐SiO2‐modified carbon/basalt hybrid FRP bars was carried out using the pultrusion‐winding process and ultrasonic treatment. The objective was to investigate the influence of the fiber volume ratio and mass fraction of nano‐SiO2 on the mechanical properties of FRP bars under drying and alkaline conditions. Molecular dynamics simulations were performed to construct interface models between fibers and epoxy resin in order to elucidate the toughening mechanism of nano‐SiO2 and the degradation mechanism in alkaline conditions. The research results show that elevating the substitution rate of carbon fibers and incorporating nano‐SiO2 doping bring about alterations in the brittle fracture characteristics of BFRP bars and enhance the post‐yield performance and interlaminar shear properties of hybrid FRP bars, concurrently mitigating the degradation of mechanical properties induced by exposure to alkaline solutions. MD simulations indicate that the addition of nano‐SiO2 improves the binding rate of hydrogen bonds between BF and epoxy resin, resulting in a higher interfacial energy between BF/epoxy resin compared to CF/epoxy resin. Moreover, nano‐SiO2 effectively mitigates the impact of alkaline solutions on the formation of interfacial hydrogen bonds. The findings of this study serve as a valuable reference for the design and application of FRP reinforced concrete structures.Highlights Nano‐SiO2 modified B/CFRP bars made via extrusion‐winding & ultrasonic treatment. Improved mechanical properties and plastic deformation in Nano‐SiO2 modified B/C FRP bars. Nano‐SiO2 inhibits B/CFRP bars degradation in alkaline solution. Nano‐SiO2 bind more at BF/epoxy than CF/epoxy interface. Nano‐SiO2 reduces alkaline solution effect on interfacial H‐bonding.

An Intelligent Apple Identification Method via the Collaboration of YOLOv5 Algorithm and Fast-Guided Filter Theory

Apple-picking robot can promote the development of smart agriculture, and accurate object recognition in complex natural environments using deep learning algorithms is critical. However, research has shown that changes in illumination and object occlusion remain significant challenges for recognition. In order to improve the accuracy of apple apple-picking robot’s identification and positioning of apples in natural environment, a method using YOLOv5 (You Only Look Once, YOLO) combined with fast-guided filter is proposed. By introducing a fast-guided filtering module, the ability to extract image features is improved, and the problem of inaccurate occlusion targets and edge detection is solved; [Formula: see text]-means clustering algorithm is introduced in improving YOLOv5, which can realize automatic adjustment of image size and step size; BiFPN structure is introduced in Neck network to add weighted feature fusion to highlight the detailed features. The results show that the algorithm proposed in this paper can well remove noise information such as occlusion edge blurring in apple images in a natural light environment. In the real orchard environment, the apple recognition accuracy rate reached 97.8%, the recall rate was 97.3% and the recognition rate was about 26.84[Formula: see text]fps. The results show that this research based on YOLOv5 and fast-guided filtering can realize fast and accurate identification of apple fruits in natural environment, and meet the practical application requirements of real-time target detection.

An accurate semantic segmentation model for bean seedlings and weeds identification based on improved ERFnet

In agricultural production activities, the growth of crops always accompanies the competition of weeds for nutrients and sunlight. In order to mitigate the adverse effects of weeds on yield, we apply semantic segmentation techniques to differentiate between seedlings and weeds, leading to precision weeding. The proposed EPAnet employs a loss function coupled with Cross-entropy loss and Dice loss to enhance attention to feature information. A multi-Decoder cooperative module based on ERFnet is designed to enhance information transfer during feature mapping. The SimAM is introduced to enhance position recognition. DO-CONV is used to replace the traditional convolution Feature Pyramid Networks (FPN) connection layer to integrate feature information, improving the model’s performance on leaf edge processing, and is named FDPN. Moreover, the Overall Accuracy has been improved by 0.65%, the mean Intersection over Union (mIoU) by 1.91%, and the Frequency-Weighted Intersection over Union (FWIoU) by 1.19%. Compared to other advanced methods, EPAnet demonstrates superior image segmentation results in complex natural environments with uneven lighting, leaf interference, and shadows.

Investigating visual navigation using spiking neural network models of the insect mushroom bodies.

Ants are capable of learning long visually guided foraging routes with limited neural resources. The visual scene memory needed for this behaviour is mediated by the mushroom bodies; an insect brain region important for learning and memory. In a visual navigation context, the mushroom bodies are theorised to act as familiarity detectors, guiding ants to views that are similar to those previously learned when first travelling along a foraging route. Evidence from behavioural experiments, computational studies and brain lesions all support this idea. Here we further investigate the role of mushroom bodies in visual navigation with a spiking neural network model learning complex natural scenes. By implementing these networks in GeNN-a library for building GPU accelerated spiking neural networks-we were able to test these models offline on an image database representing navigation through a complex outdoor natural environment, and also online embodied on a robot. The mushroom body model successfully learnt a large series of visual scenes (400 scenes corresponding to a 27 m route) and used these memories to choose accurate heading directions during route recapitulation in both complex environments. Through analysing our model's Kenyon cell (KC) activity, we were able to demonstrate that KC activity is directly related to the respective novelty of input images. Through conducting a parameter search we found that there is a non-linear dependence between optimal KC to visual projection neuron (VPN) connection sparsity and the length of time the model is presented with an image stimulus. The parameter search also showed training the model on lower proportions of a route generally produced better accuracy when testing on the entire route. We embodied the mushroom body model and comparator visual navigation algorithms on a Quanser Q-car robot with all processing running on an Nvidia Jetson TX2. On a 6.5 m route, the mushroom body model had a mean distance to training route (error) of 0.144 ± 0.088 m over 5 trials, which was performance comparable to standard visual-only navigation algorithms. Thus, we have demonstrated that a biologically plausible model of the ant mushroom body can navigate complex environments both in simulation and the real world. Understanding the neural basis of this behaviour will provide insight into how neural circuits are tuned to rapidly learn behaviourally relevant information from complex environments and provide inspiration for creating bio-mimetic computer/robotic systems that can learn rapidly with low energy requirements.

Application and mechanism of carbonate material in the treatment of heavy metal pollution: a review.

Among the many heavy metal pollution treatment agents, carbonate materials show strong flexibility and versatility by virtue of their high adsorption capacity for heavy metals and the characteristics of multiple and simple modification methods. It shows good potential for development. This review summarizes the application of carbonate materials in the treatment of heavy metal pollution according to the research of other scholars. It mainly relates to the application of surface-modified, activated, and nano-sized carbonate materials in the treatment of heavy metal pollution in water. Natural carbonate minerals and composite carbonate minerals solidify and stabilize heavy metals in soil. Solidification of heavy metals in hazardous waste solids is by MICP. There are four aspects of calcium carbonate oligomers curing heavy metals in fly ash from waste incineration. The mechanism of treating heavy metals by carbonate in different media was discussed. However, in the complex environment where multiple types of pollutants coexist, questions on how to maintain the efficient processing capacity of carbonate materials and how to use MICP to integrate heavy metal fixation and seepage prevention in solid waste base under complex and changeable natural environment deserve our further consideration. In addition, the use of carbonate materials for the purification of trace radioactive wastewater and the safe treatment of trace radioactive solid waste are also worthy of further exploration.

Template-Confined Oriented Perovskite Nanowire Arrays Enable Polarization Detection and Imaging.

Polarized light detection can effectively identify the difference between the polarization information on the target and the background, which is of great significance for detection in complex natural environments and/or extreme weather. Generally, polarized light detection inevitably relies on anisotropic structures of photodetector devices, while organic-inorganic hybrid perovskites are ideal for anisotropic patterning due to their simple and efficient preparation by solution method. Compared to patterned thin films, patterned arrays of aligned one-dimensional (1D) perovskite nanowires (PNWAs) have fewer grain boundaries and lower defect densities, making them well suited for high-performance polarization-sensitive photodetectors. Here, we fabricated PNWAs crystallographically aligned with variable line widths and alignment densities employing CD-ROM and DVD-ROM grating pattern template-confined growth (TCG) methods. The photodetectors constructed from MAPbI3 PNWAs achieved responsivity of 35.01 A/W, detectivity of 6.85 × 1013 Jones, and fast response with a rise time of 172 μs and fall time of 114 μs. They were successfully applied to high-performance polarization detection with a polarization ratio of 1.81, potentially applicable in polarized light detection systems.