Bird Species Identification using Deep Learning on GPU platform

Gavali, Pralhad; Banu, J. Saira

doi:10.1109/ic-etite47903.2020.85

Cited by 29 publications

(14 citation statements)

References 4 publications

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“…The detection part is not covered in this paper. It has already been proven through various studies that it is possible to detect birds and differentiate species using vision-based deep learning [19]- [21]. We assume that we receive a result for a numerical value (0 is not detected, 1 to n are the type of birds) by the detection result.…”

Section: The Anti-adaptive Methods Utilizing Rl Policymentioning

confidence: 99%

Anti-Adaptive Harmful Birds Repelling Method Based on Reinforcement Learning Approach

Lee

Muminov

et al. 2021

IEEE Access

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To prevent crop damage from harmful birds, various repelling methods have been studied. However, harmful birds are still causing damage in the orchard by adapting to the repelling device according to their biological characteristics. This paper proposes a method called Anti-adaptive Harmful Birds Repelling (AHBR) that uses the model-free learning idea of the Reinforcement Learning (RL) approach to repell harmful birds that can effectively prevent bird adaptation problems. To prevent adaptation, the AHBR method uses a method of learning the bird's reaction to the available threat sounds and playing them in patterns that are difficult to adapt through the RL approach. We also proposed the Long-term and Shortterm (LaS) policy to meet the Markov assumptions that make RL difficult to implement in the real world. The LaS policy enable learning of the actual bird's reaction to the sound of a threat. The performance of the AHBR method was evaluated in a closed environment to experiment real harmful bird such as Brown-eared Bulbul, Great Tit, and Eurasian Magpie captured in orchards. Results obtained from the experiment showed that the AHBR method was on average 43.5% better than the threat sound patterns(One, Sequential, Reverse Sequential, Random) used in commercial products.INDEX TERMS Agricultural engineering. Machine learning. Intelligent systems. Automation. Antiadaptive repeller

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Section: The Anti-adaptive Methods Utilizing Rl Policymentioning

confidence: 99%

Anti-Adaptive Harmful Birds Repelling Method Based on Reinforcement Learning Approach

Lee

Muminov

et al. 2021

IEEE Access

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“…Regardless of the image sensor used, it is challenging to distinguish birds from other flying objects such as insects, drones, or airplanes. Therefore, the Deep Learning approach is deemed as a suitable tool for bird identification [4,28,[42][43][44][45][46]. The comparative analysis of AI-based methods used for bird identification is presented in Table 2.…”

Section: Identification Algorithmsmentioning

confidence: 99%

Comprehensive Bird Preservation at Wind Farms

Gradolewski

Dziak

Martynow³

et al. 2021

Sensors

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Wind as a clean and renewable energy source has been used by humans for centuries. However, in recent years with the increase in the number and size of wind turbines, their impact on avifauna has become worrisome. Researchers estimated that in the U.S. up to 500,000 birds die annually due to collisions with wind turbines. This article proposes a system for mitigating bird mortality around wind farms. The solution is based on a stereo-vision system embedded in distributed computing and IoT paradigms. After a bird’s detection in a defined zone, the decision-making system activates a collision avoidance routine composed of light and sound deterrents and the turbine stopping procedure. The development process applies a User-Driven Design approach along with the process of component selection and heuristic adjustment. This proposal includes a bird detection method and localization procedure. The bird identification is carried out using artificial intelligence algorithms. Validation tests with a fixed-wing drone and verifying observations by ornithologists proved the system’s desired reliability of detecting a bird with wingspan over 1.5 m from at least 300 m. Moreover, the suitability of the system to classify the size of the detected bird into one of three wingspan categories, small, medium and large, was confirmed.

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“…The core component of each vision based system is a detection algorithm. The bird detection in the video stream can be made using motion detection [ 22 , 25 , 43 ], AI based identification [ 26 , 44 , 45 , 46 , 47 , 48 , 49 ], or a combination of both [ 10 , 38 ]. Whereas the motion detection algorithms allow the reduction of the computational complexity of the safety system [ 50 ], the application of AI methods allows bird identification [ 48 , 49 ] and the reduction of false positive rates [ 10 ].…”

Section: Background and Related Workmentioning

confidence: 99%

A Runway Safety System Based on Vertically Oriented Stereovision

Gradolewski

Dziak²,

Kaniecki³

et al. 2021

Sensors

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In 2020, over 10,000 bird strikes were reported in the USA, with average repair costs exceeding $200 million annually, rising to $1.2 billion worldwide. These collisions of avifauna with airplanes pose a significant threat to human safety and wildlife. This article presents a system dedicated to monitoring the space over an airport and is used to localize and identify moving objects. The solution is a stereovision based real-time bird protection system, which uses IoT and distributed computing concepts together with advanced HMI to provide the setup’s flexibility and usability. To create a high degree of customization, a modified stereovision system with freely oriented optical axes is proposed. To provide a market tailored solution affordable for small and medium size airports, a user-driven design methodology is used. The mathematical model is implemented and optimized in MATLAB. The implemented system prototype is verified in a real environment. The quantitative validation of the system performance is carried out using fixed-wing drones with GPS recorders. The results obtained prove the system’s high efficiency for detection and size classification in real-time, as well as a high degree of localization certainty.

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Bird Species Identification using Deep Learning on GPU platform

Cited by 29 publications

References 4 publications

Anti-Adaptive Harmful Birds Repelling Method Based on Reinforcement Learning Approach

Anti-Adaptive Harmful Birds Repelling Method Based on Reinforcement Learning Approach

Comprehensive Bird Preservation at Wind Farms

A Runway Safety System Based on Vertically Oriented Stereovision

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