Bird Recognition Research Articles

Automatic classification of bird species related to power line faults using deep convolution features and ECOC‐SVM model

AbstractBird‐related outages greatly threaten the safety of overhead transmission and distribution lines, while electrocution and collisions of birds with power lines, especially endangered species, are significant environmental concerns. Automatic bird recognition can be helpful to mitigate this contradiction. This paper proposes a method for automatic classification of bird species related to power line faults combining deep convolution features with error‐correcting output codes support vector machine (ECOC‐SVM). An image dataset of about 20 high‐risk and 20 low‐risk bird species was constructed, and the feed‐forward denoising convolutional neural network was used for image preprocessing. The deep convolution features of bird images were extracted by DarkNet‐53, and taken as inputs of the ECOC‐SVM for model training and bird species classification. The gradient‐weighted class activation mapping was used for visual explanations of the model decision region. The experimental results indicate that the average accuracy of the proposed method can reach 94.39%, and its performance was better than other models using different feature extraction networks and classification algorithms.

A Cross-Modal Semantic Alignment and Feature Fusion Method for Bionic Drone and Bird Recognition

With the continuous progress in drone and materials technology, numerous bionic drones have been developed and employed in various fields. These bionic drones are designed to mimic the shape of birds, seamlessly blending into the natural environment and reducing the likelihood of detection. However, such a high degree of similarity also poses significant challenges in accurately distinguishing between real birds and bionic drones. Existing methods attempt to recognize both using optical images, but the visual similarity often results in poor recognition accuracy. To alleviate this problem, in this paper, we propose a cross-modal semantic alignment and feature fusion (CSAFF) network to improve the recognition accuracy of bionic drones. CSAFF aims to introduce motion behavior information as an auxiliary cue to improve discriminability. Specifically, a semantic alignment module (SAM) was designed to explore the consistent semantic information between cross-modal data and provide more semantic cues for the recognition of bionic drones and birds. Then, a feature fusion module (FFM) was developed to fully integrate cross-modal information, which effectively enhances the representability of these features. Extensive experiments were performed on datasets containing bionic drones and birds, and the experimental results consistently show the effectiveness of the proposed CSAFF method in identifying bionic drones and bionic birds.

Implementation of a Cuckoo Search intelligent simulation system for node placement problem in Wireless Mesh Networks: Performance evaluation for tuning hyperparameters [formula omitted] and [formula omitted]

In recent years, Internet of Things (IoT) is the hottest topics in the research and implementation of wireless networks. Most of the research of IoT-based applications is related with the data collecting and processing using sensors and actuators. As the network environment for connecting IoT devices are considered WLAN or Bluetooth. The Wireless Mesh Networks (WMNs) are the most suitable networks for the Internet of Things (IoT) due to their numerous benefits. However, they have several issues related to wireless communications. One solution is the optimization of position and location of mesh routers. But finding the best location of mesh routers is an NP-hard problem. To deal with this issue, we propose and implement an intelligent simulation system based on the Cuckoo Search (CS) algorithm called WMN-CS. In the implemented system, the hyperparameters should be tuned to find better solutions. This paper evaluates the WMN-CS performance for various combination of hyperparameters by tuning host bird discovery rate (pa) and scale parameter (γ). The simulation results show that a better performance is achieved when the scale parameter (γ) is between 0.07 and 0.09 and the host bird recognition rate (pa) is between 0.900 and 0.925.

Recognition of European mammals and birds in camera trap images using deep neural networks

Recognition of European mammals and birds in camera trap images using deep neural networks

Assessing the potential of BirdNET to infer European bird communities from large-scale ecoacoustic data

1.Passive acoustic monitoring has become increasingly popular as a practical and cost-effective way of obtaining highly reliable acoustic data in ecological research projects. Increased ease of collecting these data means that, currently, the main bottleneck in ecoacoustic monitoring projects is often the time required for the manual analysis of passively collected recordings. In this study we evaluate the potential and current limitations of BirdNET-Analyzer v2.4, the most advanced and generic deep learning algorithm for bird recognition to date, as a tool to assess bird community composition through the automated analysis of large-scale ecoacoustic data.2.To this end, we study 3 acoustic datasets comprising a total of 629 environmental soundscapes collected in 194 different sites spread across a 19° latitude span in Europe. We analyze these soundscapes using both BirdNET and manual listening by local expert birders, and we then compare the results obtained through the two methods to evaluate the performance of the algorithm both at the level of each single vocalization and for entire recording sequences (1, 5 or 10 min).3.Since BirdNET provides a confidence score for each identification, minimum confidence thresholds can be used to filter out identifications with low scores, thus retaining only the most reliable ones. The volume of ecoacoustic data used in this study did not allow us to estimate species-specific minimum confidence thresholds for most taxa, so we instead used and evaluated global confidence thresholds selected for optimized results when consistently applied across all species.4.Our analyses reveal that BirdNET identifications can be highly reliable if a sufficiently high minimum confidence threshold is used. However, the inevitable trade-off between precision and recall does not allow to obtain satisfactory results for both metrics at the same time. We found that F1-scores remain moderate (<0.5) for all datasets and confidence thresholds studied, and that acoustic datasets of extended duration seem to be currently necessary for BirdNET to provide a reliable and minimally comprehensive picture of the target bird community. We estimate, however, that the usage of species- and context-specific minimum confidence thresholds would allow to substantially improve the global performance benchmarks obtained in this study.5.We conclude that a judicious use of AI-based identifications provided by BirdNET can represent a powerful method to assist in the assessment of bird community composition through the automated analysis of ecoacoustic data, especially when applied to acoustic datasets of extended duration.

A genomic basis of vocal rhythm in birds

Vocal rhythm plays a fundamental role in sexual selection and species recognition in birds, but little is known of its genetic basis due to the confounding effect of vocal learning in model systems. Uncovering its genetic basis could facilitate identifying genes potentially important in speciation. Here we investigate the genomic underpinnings of rhythm in vocal non-learning Pogoniulus tinkerbirds using 135 individual whole genomes distributed across a southern African hybrid zone. We find rhythm speed is associated with two genes that are also known to affect human speech, Neurexin-1 and Coenzyme Q8A. Models leveraging ancestry reveal these candidate loci also impact rhythmic stability, a trait linked with motor performance which is an indicator of quality. Character displacement in rhythmic stability suggests possible reinforcement against hybridization, supported by evidence of asymmetric assortative mating in the species producing faster, more stable rhythms. Because rhythm is omnipresent in animal communication, candidate genes identified here may shape vocal rhythm across birds and other vertebrates.

SDFIE-NET – A self-learning dual-feature fusion information capture expression method for birdsong recognition

Bird recognition is important for the monitoring of bird populations and the protection of ecosystems. Identifying birds through image forms can be difficult due to the complexity of natural environments. Song-based bird recognition allows for bird identification with only a small amount of background noise introduced, however, efficiently recognizing bird songs remains a challenging task. Based on this problem, this paper proposed a self-learning dual-feature fusion information capture expression method (SDFIE-NET) for recognizing birdsong. Firstly, using the Mel filter excerpt the low-frequency characteristics of the bird song. Since fixed-parameter filters are incapable of achieving different feature extraction effects based on different birdsong. In this paper, we incorporate a fully learnable audio classification front-end Leaf architecture for the extraction of bird song feature information, which can self-learn different extraction parameters for the birdsong. Effectively combining the high-frequency feature information and low-frequency differences acquired by the two approaches corresponds to the declared dual-feature fusion module (SCDFF), reducing information redundancy and improving characterization capability. Secondly, the backbone network utilizes SDFIE-NET, which is composed of the Fused-MBConv module and modified CA-MBConv module. The Criss-Cross Attention module is added after each layer composed of Fused-MBConv modules. This improves the speed and accuracy of effective information transfer between internal modules and increases the expressive power of the model at the pixel level. To enhance the anti-interference and generalization ability of the model, we constructed a self-made dataset (Bird_alldata) consisting of 30 kinds of birdsong. On this dataset, we performed a variety of experiments, and recognition accuracy reached 95.77 % and the F1-score reached 95.52 %. Generalization experiments were conducted on the environmental sound dataset Urbansound8K and the bird song dataset Birdsdata, and the model achieves recognition accuracies of 94.05 % and 94.10 % on the two datasets, with F1-scores of 94.21 % and 94.05 %, respectively.

A digital twin-based energy-efficient wireless multimedia sensor network for waterbirds monitoring

Wetlands play a critical role in maintaining the global climate, regulating the hydrological cycle, and protecting human health. However, they are rapidly disappearing due to human activities. Waterbirds are valuable bio-indicators of wetland health, but it is challenging to monitor them effectively. Wireless Multimedia Sensor Networks (WMSNs) offer a promising technology for monitoring wetlands. Nonetheless, these networks are constrained in terms of energy, and also encounter challenges associated with large-scale deployments under natural environmental conditions. These conditions introduce harsh circumstances that may not have been anticipated during the pre-deployment testing phase. This paper proposes a Digital Twin (DT) based energy-efficient WMSN monitoring system specifically tailored for waterbirds in wetlands. The system utilizes a unique approach that combines local audio identification and image compression with DT technology to optimize network performance and minimize energy consumption. To reduce unnecessary image transmissions, the system employs a real-time, low-complexity local audio identification phase before triggering image capture. A denoising step is employed to achieve highly accurate bird recognition despite surrounding noises. Each image undergoes a low-complexity compression scheme prior to transmission, further enhancing energy efficiency. To enhance the system’s overall efficiency and effectiveness, DT technology is integrated to create real-time replicas of the WMSN and the monitoring application. A synergistic interaction between the two DTs enables cooperative data-making decision that ensures both QoS (Quality of Service) and QoE (Quality of Experience) requirements are met. Transmission rate control is done using a fuzzy logic decision-making technique. Real-time feedback provides rapid and accurate analysis of the current state of the WMSN, allowing for dynamic adjustments. The ”what-if scenarios” feature of the implemented DTs has been effectively leveraged to find the most suitable settings for the controller. The effectiveness and performance enhancements achieved by integrating DT into our WMSN-based surveillance system are validated through comprehensive experiments in scenarios that correspond to a real-world wetland. Comparative analyses demonstrate the undeniable benefits of the DT-integrated system compared to a conventional WMSN-based surveillance setup. In particular, the results demonstrate the system’s superior performance in terms of energy efficiency, real-time monitoring capabilities, and ability to handle multiple video sources.

PPA‐ResNet: An Airport Bird Recognition Model Based on Improved ResNet

With the development of air transport and improved ecological environment, bird strikes on aircraft have become a significant threat to flight safety. Accurate and efficient identification of airport birds can enhance the efficiency of airport staff in bird strike prevention. Public datasets for bird classification are typically limited in size and not specifically tailored for airport birds. Therefore, this paper introduces a self‐built airport bird dataset, ADB‐20, and proposes an image recognition method based on an improved ResNet model. First, this paper replaces traditional convolution in the residual structure with the Pyramidal Convolution method, ensuring the extraction of multiscale features. Second, it introduces the Parallel Convolutional Block Attention Moduleto the backbone network, considering features in both channel and spatial dimensions. Last, the Atrous Spatial Pyramid Pooling module is incorporated to capture contextual information at various scales. Experimental results show that the PPA‐ResNet model achieves an accuracy of 95.2%, with a recall of 93.7%, a precision of 96.8%, and an F1 score of 95.2% on the ADB‐20 dataset. The proposed algorithm significantly enhances classification performance compared to other mainstream image classification algorithms. These indicators confirm that the results of this study can precisely identify airport birds, aid airport personnel in making informed decisions, and guarantee the safety of aviation transport.

Rare bird recognition method in Beijing based on TC-YOLO model

Rare bird recognition method in Beijing based on TC-YOLO model

Drone audition for bioacoustic monitoring

Abstract Multi‐rotor drones equipped with acoustic sensors have great potential for bioacoustically monitoring vocal species in the environment for biodiversity conservation. The bottleneck of this emerging technology is the ego‐noise from the rotating motors and propellers, which can completely mask the target sound and make sound recordings unusable for further analysis. The ego‐noise not only degrades the performance of bioacoustic monitoring but also impacts the behaviour of target species if the drone is too close to the target area. In this paper, we address this challenging problem by combining hardware and software solutions that minimize the impact of drone ego‐noise on bioacoustic monitoring. To collect the target sound from the ground, we used a shotgun microphone recording system suspended underneath the drone body with a wire rope (steel fishing line) of length 2 m. The suspended rope puts a large distance between the drone and the recorder, reducing the propeller sound perceived by the microphone. The shotgun microphone enables the sound to be picked up from the ground effectively while rejecting the drone sound from above. We further developed a software solution that aims to automatically recognize the bird species from the bird call recording and we proposed a noise‐augmented training scheme to improve the robustness of bird recognition in the presence of strong drone noise. We evaluated the performance of the system in a test problem of recognizing 20 bird species with in‐flight recordings, where a loudspeaker on the ground simulates bird calls. The recordings were obtained using a drone hovering at various altitudes ranging from 5 to 30 m. By combining the hardware and software solutions, the system recognizes birds robustly at an altitude of 30 m and signal‐to‐noise ratio −25 dB. This demonstrates the feasibility of our drone audition system for bioacoustic monitoring. The proposed method overcomes a long‐standing bottleneck problem in drone audition and promises new applications of bioacoustic monitoring in research and management.

Research on Fine-Grained Image Recognition of Birds Based on Improved YOLOv5.

Birds play a vital role in maintaining biodiversity. Accurate identification of bird species is essential for conducting biodiversity surveys. However, fine-grained image recognition of birds encounters challenges due to large within-class differences and small inter-class differences. To solve this problem, our study took a part-based approach, dividing the identification task into two parts: part detection and identification classification. We proposed an improved bird part detection algorithm based on YOLOv5, which can handle partial overlap and complex environmental conditions between part objects. The backbone network incorporates the Res2Net-CBAM module to enhance the receptive fields of each network layer, strengthen the channel characteristics, and improve the sensitivity of the model to important information. Additionally, in order to boost data on features extraction and channel self-regulation, we have integrated CBAM attention mechanisms into the neck. The success rate of our suggested model, according to experimental findings, is 86.6%, 1.2% greater than the accuracy of the original model. Furthermore, when compared with other algorithms, our model's accuracy shows noticeable improvement. These results show how useful the method we suggested is for quickly and precisely recognizing different bird species.

Bird Object Detection: Dataset Construction, Model Performance Evaluation, and Model Lightweighting.

The application of object detection technology has a positive auxiliary role in advancing the intelligence of bird recognition and enhancing the convenience of bird field surveys. However, challenges arise due to the absence of dedicated bird datasets and evaluation benchmarks. To address this, we have not only constructed the largest known bird object detection dataset, but also compared the performances of eight mainstream detection models on bird object detection tasks and proposed feasible approaches for model lightweighting in bird object detection. Our constructed bird detection dataset of GBDD1433-2023, includes 1433 globally common bird species and 148,000 manually annotated bird images. Based on this dataset, two-stage detection models like Faster R-CNN and Cascade R-CNN demonstrated superior performances, achieving a Mean Average Precision (mAP) of 73.7% compared to one-stage models. In addition, compared to one-stage object detection models, two-stage object detection models have a stronger robustness to variations in foreground image scaling and background interference in bird images. On bird counting tasks, the accuracy ranged between 60.8% to 77.2% for up to five birds in an image, but this decreased sharply beyond that count, suggesting limitations of object detection models in multi-bird counting tasks. Finally, we proposed an adaptive localization distillation method for one-stage lightweight object detection models that are suitable for offline deployment, which improved the performance of the relevant models. Overall, our work furnishes an enriched dataset and practice guidelines for selecting suitable bird detection models.

Pairwise Guided Multilayer Cross-Fusion Network for Bird Image Recognition

Bird identification is the first step in collecting data on bird diversity and abundance, which also helps research on bird distribution and population measurements. Most research has built end-to-end training models for bird detection task via CNNs or attentive models, but many perform unsatisfactorily in fine-grained bird recognition. Bird recognition tasks are highly influenced by factors, including the similar appearance of different subcategories, diverse bird postures, and other interference factors such as tree branches and leaves from the background. To tackle this challenge, we propose the Progressive Cross-Union Network (PC-Net) to capture more subtle parts with low-level attention maps. Based on cross-layer information exchange and pairwise learning, the proposed method uses two modules to improve feature representation and localization. First, it utilizes low- and high-level information for cross-layer feature fusion, which enables the network to extract more comprehensive and discriminative features. Second, the network incorporates deep semantic localization to identify and enhance the most relevant regions in the images. In addition, the network is designed with a semantic guidance loss to improve its generalization for variable bird poses. The PC-Net was evaluated on an extensively used birds dataset (CUB-200-2011), which contains 200 birds subcategories. The results demonstrate that the PC-Net achieved an impressive recognition accuracy of 89.2%, thereby outperforming maintained methods in bird subcategory identification. We also achieved competitive results on two other datasets with data on cars and airplanes. The results indicated that the PC-Net improves the accuracy of diverse bird recognition, as well as other fine-grained recognition scenarios.

Bird Species Identification Using CNN

In our world, there are above 11000 bird species. Some bird species are being found rarely. Bird identification is a challenging task that usually leads to unclear labelling. When presented a picture of a bird, even professional bird watchers differ on the species. Despite having the same basic components across all bird species, form and appearance can vary greatly. Intraclass variance is substantial due to variations in lighting and backdrop, as well as a wide range of instances. Additionally, visual recognition of birds by humans is more comprehensible than audible recognition of birds. Consequently, we utilised convolutional neural networks (CNN). CNNs are a powerful Deep Learning ensemble that have shown to be effective in image processing. The dataset is used for both training and testing of a CNN system that classifies bird species. Everyone can quickly determine the name of the bird they wish to know by following this strategy. Keywords— Bird species, Image Processing, Convolutional Neural Networks.

Automatic individual recognition of wild Crested Ibis based on hybrid method of self-supervised learning and clustering

Automatic individual recognition of wild Crested Ibis is challenged by two factors: the lack of labeled data and an unpredictable number of individuals. In this paper, we propose a hybrid method of self-supervised learning and clustering to automatically recognize wild Crested Ibis based on vocalizations. To address the first challenge, we enhance the Bootstrap Your Own Latent for Audio (BYOL-A) model by using an improved augmentation module and Spatial Group-wise Enhance (SGE) attention module to create the self-supervised learning model BYOL-AIS. This model aims to extract a more discriminative representation of Crested Ibis vocalizations. To handle the second challenge, we introduce a clustering method that combines Uniform Manifold Approximation and Projection (UMAP) and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to distinguish the representation of Crested Ibis vocalizations. We evaluate our proposed method using vocalizations collected from 10 Crested Ibis individuals and achieve a recognition accuracy of 0.864. This accuracy is comparable to the performance of commonly used supervised methods. Our results suggest that our proposed method is a feasible method for wild bird recognition in the absence of labeled data and has the potential to be an analytical tool for processing huge amounts of monitoring data.

PSEV-BF Methodology for Object Recognition of Birds in Uncontrolled Environments

Computer vision methodologies using machine learning techniques usually consist of the following phases: pre-processing, segmentation, feature extraction, selection of relevant variables, classification, and evaluation. In this work, a methodology for object recognition is proposed. The methodology is called PSEV-BF (pre-segmentation and enhanced variables for bird features). PSEV-BF includes two new phases compared to the traditional computer vision methodologies, namely: pre-segmentation and enhancement of variables. Pre-segmentation is performed using the third version of YOLO (you only look once), a convolutional neural network (CNN) architecture designed for object detection. Additionally, a simulated annealing (SA) algorithm is proposed for the selection and enhancement of relevant variables. To test PSEV-BF, the repository commons object in Context (COCO) was used with images exhibiting uncontrolled environments. Finally, the APIoU metric (average precision intersection over union) is used as an evaluation benchmark to compare our methodology with standard configurations. The results show that PSEV-BF has the highest performance in all tests.

A Fine-Grained Bird Classification Method Based on Attention and Decoupled Knowledge Distillation.

Classifying birds accurately is essential for ecological monitoring. In recent years, bird image classification has become an emerging method for bird recognition. However, the bird image classification task needs to face the challenges of high intraclass variance and low inter-class variance among birds, as well as low model efficiency. In this paper, we propose a fine-grained bird classification method based on attention and decoupled knowledge distillation. First of all, we propose an attention-guided data augmentation method. Specifically, the method obtains images of the object's key part regions through attention. It enables the model to learn and distinguish fine features. At the same time, based on the localization-recognition method, the bird category is predicted using the object image with finer features, which reduces the influence of background noise. In addition, we propose a model compression method of decoupled knowledge distillation. We distill the target and nontarget class knowledge separately to eliminate the influence of the target class prediction results on the transfer of the nontarget class knowledge. This approach achieves efficient model compression. With 67% fewer parameters and only 1.2 G of computation, the model proposed in this paper still has a 87.6% success rate, while improving the model inference speed.

Automated System for Bird Species Identification Using CNN

There are around 11000 different bird species in the globe. Rarely are certain bird species encountered. Bird identification is a challenging task that usually leads to unclear labelling. When presented a picture of a bird, even professional bird watchers differ on the species. Despite having the same basic components across all bird species, form and appearance can vary greatly. Intraclass variance is substantial due to variations in lighting and backdrop, as well as a wide range of instances. Additionally, visual recognition of birds by humans is more comprehensible than audible recognition of birds. Consequently, the convolutional neural networks(CNN) is utilized for an automated bird species identification system. CNNs are a powerful Deep Learning ensemble that have shown to be effective in image processing. The dataset is used for both training and testing of a CNN system that classifies bird species. This will lead to quick identification of the bird species using an automated process.

Bird sound recognition based on adaptive frequency cepstral coefficient and improved support vector machine using a hunter-prey optimizer.

Bird sound recognition is crucial for bird protection. As bird populations have decreased at an alarming rate, monitoring and analyzing bird species helps us observe diversity and environmental adaptation. A machine learning model was used to classify bird sound signals. To improve the accuracy of bird sound recognition in low-cost hardware systems, a recognition method based on the adaptive frequency cepstrum coefficient and an improved support vector machine model using a hunter-prey optimizer was proposed. First, in sound-specific feature extraction, an adaptive factor is introduced into the extraction of the frequency cepstrum coefficients. The adaptive factor was used to adjust the continuity, smoothness and shape of the filters. The features in the full frequency band are extracted by complementing the two groups of filters. Then, the feature was used as the input for the following support vector machine classification model. A hunter-prey optimizer algorithm was used to improve the support vector machine model. The experimental results show that the recognition accuracy of the proposed method for five types of bird sounds is 93.45%, which is better than that of state-of-the-art support vector machine models. The highest recognition accuracy is obtained by adjusting the adaptive factor. The proposed method improved the accuracy of bird sound recognition. This will be helpful for bird recognition in various applications.