Unmanned aerial vehicles (UAVs) have become increasingly popular in recent years for both commercial and recreational purposes. Regrettably, the security of people and infrastructure is also clearly threatened by this increased demand. To address the current security challenge, much research has been carried out and several innovations have been made. Many faults still exist, however, including type or range detection failures and the mistaken identification of other airborne objects (for example, birds). A standard dataset that contains photos of drones and birds and on which the model might be trained for greater accuracy is needed to conduct experiments in this field. The supplied dataset is crucial since it will help train the model, giving it the ability to learn more accurately and make better decisions. The dataset that is being presented is comprised of a diverse range of images of birds and drones in motion. Pexel website's images and videos have been used to construct the dataset. Images were obtained from the frames of the recordings that were acquired, after which they were segmented and augmented with a range of circumstances. This would improve the machine-learning model's detection accuracy while increasing dataset training. The dataset has been formatted according to the YOLOv7 PyTorch specification. The test, train, and valid folders are contained within the given dataset. These folders each feature a plaintext file that corresponds to an associated image. Relevant metadata regarding the discovered object is described in the plaintext file. Images and labels are the two subfolders that constitute the folders. The collection consists of 20,925 images of birds and drones. The images have a 640 x 640 pixel resolution and are stored in JPEG format.

YOLO-based Segmented Dataset for Drone vs. Bird Detection for Deep and Machine Learning Algorithms

Unmanned aerial vehicles (UAVs), or drones, have witnessed a sharp rise in both commercial and recreational use, but this surge has brought about significant security concerns. Drones, when misidentified or undetected, can pose risks to people, infrastructure, and air traffic, especially when confused with other airborne objects, such as birds. To overcome this challenge, accurate detection systems are essential. However, a reliable dataset for distinguishing between drones and birds has been lacking, hindering the progress of effective models in this field.

This dataset is designed to fill this gap, enabling the development and fine-tuning of models to better identify drones and birds in various environments. The dataset comprises a diverse collection of images, sourced from Pexel’s website, representing birds and drones in motion. These images were captured from video frames and are segmented, augmented, and pre-processed to simulate different environmental conditions, enhancing the model's training process.

Formatted in accordance with the YOLOv7 PyTorch specification, the dataset is organized into three folders: Test, Train, and Valid. Each folder contains two sub-folders—*Images* and Labels—with the Labels folder including the associated metadata in plaintext format. This metadata provides valuable information about the detected objects within each image, allowing the model to accurately learn and detect drones and birds in varying circumstances. The dataset contains a total of 20,925 images, all with a resolution of 640 x 640 pixels in JPEG format, providing comprehensive training and validation opportunities for machine learning models.

• Test Folder: Contains 889 images (both drone and bird images). The folder has sub-categories marked as BT (Bird Test Images) and DT (Drone Test Images).

• Train Folder: With a total of 18,323 images, this folder includes both drone and bird images, also categorized as BT and DT.

• Valid Folder: Consisting of 1,740 images, the images in this folder are similarly categorized into BT and DT.

This dataset is essential for training more accurate models that can differentiate between drones and birds in real-time applications, thereby improving the reliability of drone detection systems for enhanced security and efficiency.

Smart Orchard Bird Deterrent Drone Market Outlook

According to our latest research, the global Smart Orchard Bird Deterrent Drone market size reached USD 412 million in 2024, reflecting the rapid adoption of advanced agricultural technologies worldwide. The market is set to grow at a robust CAGR of 17.1% from 2025 to 2033, with the forecasted market size expected to reach USD 1,453 million by 2033. This remarkable growth is primarily driven by the increasing demand for precision agriculture solutions and the urgent need to mitigate crop losses caused by bird infestations in high-value orchards. As per our latest research findings, technological advancements in drone autonomy, integration of AI-based deterrence systems, and the pressing need for sustainable crop protection are the pivotal factors fueling this market's expansion.

The surge in demand for smart orchard bird deterrent drones can be attributed to the escalating economic losses suffered by fruit, nut, and vineyard growers due to bird damage. Traditional bird deterrence methods such as netting, acoustic devices, and scarecrows have often proven to be either labor-intensive, environmentally unfriendly, or only temporarily effective. The advent of drones equipped with advanced sensors, AI-driven flight patterns, and multi-modal deterrent systems has revolutionized crop protection by providing dynamic, non-lethal, and highly efficient solutions. Growers are increasingly investing in these smart systems to maximize yield, reduce labor costs, and ensure compliance with environmental regulations regarding wildlife protection. The market's growth is further bolstered by increasing awareness campaigns, government support for precision agriculture, and the proven return on investment offered by these technologies.

Another significant growth driver is the integration of autonomous and semi-autonomous capabilities within these drones, which has drastically reduced the operational complexities for end-users. Modern smart orchard bird deterrent drones leverage machine learning algorithms, real-time data analytics, and GPS-based navigation to autonomously patrol orchards, identify bird flocks, and deploy customized deterrence measures. This autonomy not only enhances operational efficiency but also enables coverage of larger orchard areas with minimal human intervention. As labor shortages and rising wage costs continue to challenge the agricultural sector, these drones present an attractive solution for large-scale commercial growers and cooperatives alike. Furthermore, the scalability and adaptability of these systems to different crop types and orchard layouts make them suitable for a wide range of agricultural applications.

The growing focus on sustainable agriculture and environmental stewardship is also catalyzing the adoption of smart orchard bird deterrent drones. Unlike chemical repellents and physical barriers, drones provide a non-invasive and eco-friendly method to mitigate bird damage. The ability to program variable deterrent strategies, such as audio-visual signals, targeted flight paths, and species-specific responses, ensures minimal disruption to non-target wildlife and local ecosystems. Regulatory bodies across North America, Europe, and Asia Pacific are increasingly endorsing such precision technologies as part of integrated pest management (IPM) frameworks. The cumulative effect of these factors is creating a fertile environment for the rapid expansion of the smart orchard bird deterrent drone market globally.

Regionally, North America and Europe are leading the adoption of smart orchard bird deterrent drones, owing to their advanced agricultural infrastructure, higher disposable incomes among growers, and strong emphasis on technological innovation. Asia Pacific, however, is emerging as the fastest-growing market, driven by the rapid expansion of high-value fruit and nut orchards in China, Australia, and India. The availability of government subsidies, growing export-oriented horticulture, and increasing awareness about crop protection technologies are propelling the adoption of these drones in the region. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as local growers increasingly recognize the economic and environmental benefits of smart bird deterrence solutions.

Product Type Analysis

The Product Type segment of the smart orchard bird deterrent d

The global bird dispersal systems market is experiencing robust growth, driven by increasing concerns about bird-related aviation hazards, agricultural damage, and public health issues. The market, estimated at $500 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching approximately $850 million by 2033. This expansion is fueled by several key factors. Firstly, the rising adoption of acoustic and laser-based bird dispersal systems in airports worldwide is a significant contributor. Stringent aviation safety regulations and the increasing number of air travel passengers are driving demand for effective bird deterrent technologies. Secondly, the agricultural sector is increasingly adopting bird dispersal solutions to protect crops from significant losses caused by bird infestations. Finally, advancements in technology, leading to the development of more sophisticated and efficient systems (like drone-based solutions), are further boosting market growth. The market segmentation reveals a strong preference for acoustic systems, given their cost-effectiveness and widespread applicability. However, laser and drone-based systems are gaining traction due to their targeted approach and higher effectiveness in specific scenarios. Despite the positive outlook, market growth faces certain constraints. High initial investment costs for advanced systems like laser and drone-based technologies can hinder adoption, especially among smaller farms and airports. Furthermore, the effectiveness of bird dispersal systems can vary depending on bird species and environmental factors, presenting a challenge for consistent results. Regulatory hurdles and environmental concerns related to certain technologies also need consideration. Geographically, North America and Europe currently hold a significant market share, reflecting the higher adoption rates in these regions. However, the Asia-Pacific region is anticipated to witness the most substantial growth in the coming years, fueled by increasing industrialization and rising awareness of bird-related issues in developing economies. The competitive landscape is dynamic, with several established players alongside innovative startups offering a range of solutions to meet the diverse needs of various applications.

Drone-Assisted Crowd Monitoring Market Outlook

As per our latest research, the global drone-assisted crowd monitoring market size has reached USD 2.3 billion in 2024, reflecting the rapidly growing integration of unmanned aerial vehicles in public safety and event management. The market is expected to expand at a robust CAGR of 18.2% from 2025 to 2033, projecting a value of USD 11.8 billion by 2033. This remarkable growth is primarily driven by increasing demand for real-time surveillance, advancements in AI-based analytics, and the urgent need for efficient crowd management solutions across various sectors.

The surge in demand for drone-assisted crowd monitoring is largely attributed to the growing focus on public safety and security amid rising incidents of civil unrest, large-scale events, and natural disasters. Drones equipped with advanced sensors, high-resolution cameras, and AI-powered analytics provide authorities with the ability to monitor vast crowds in real time, enabling quick response to emergencies and potential threats. The integration of thermal imaging and real-time surveillance technology has further enhanced the ability of drones to operate in challenging environments, such as nighttime or adverse weather conditions, making them indispensable tools for law enforcement and disaster response agencies. Additionally, regulatory support and the reduction in drone hardware costs have encouraged widespread adoption, particularly in developed regions.

Another significant growth factor is the increasing utilization of drones in event management and traffic monitoring. Large-scale public gatherings, such as concerts, sports events, and festivals, require efficient crowd control to prevent stampedes and ensure overall safety. Drones provide a bird’s-eye view, allowing event organizers and security personnel to monitor crowd density, identify bottlenecks, and deploy resources effectively. In urban areas, drones are also being used for traffic monitoring, helping authorities manage congestion and respond to incidents swiftly. The commercial sector, including private security firms and event management companies, is increasingly recognizing the value of drone-assisted solutions, further fueling market growth.

Technological advancements play a pivotal role in propelling the drone-assisted crowd monitoring market. The integration of AI-based analytics enables drones to process large volumes of visual data, automatically detect anomalies, and generate actionable insights. This reduces the workload on human operators and enhances the accuracy of threat detection. Moreover, the development of lightweight, long-endurance drones equipped with advanced communication systems has expanded the operational capabilities of these platforms. The ongoing evolution of drone regulations, particularly in North America and Europe, is fostering innovation and encouraging the deployment of drones for a wider range of applications, from public safety to commercial uses.

Regionally, North America dominates the market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The presence of major technology providers, robust regulatory frameworks, and high investments in public safety infrastructure have established North America as a key hub for drone-assisted crowd monitoring solutions. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period, driven by increasing urbanization, rising security concerns, and government initiatives to modernize surveillance systems. Emerging economies in Latin America and the Middle East & Africa are also gradually adopting drone-based crowd monitoring solutions, albeit at a slower pace due to regulatory and infrastructural challenges.

Hardware Analysis

The hardware segment forms the backbone of the drone-assisted crowd monitoring market, encompassing UAV platforms, cameras, sensors, communication modules, and power systems. The rapid evolution of drone hardware, including the development of lightweight airframes, high-capacity batteries, and advanced propulsion systems, has significantly enhanced the operational efficiency and endurance of drones used for crowd monitoring. High-resolution cameras and thermal imaging sensors are now standard features, enabling drones to capture detailed imagery and video feeds under various lighting and weather conditions. The growing availability of modular drone designs allows users to customize

The TrajNet Challenge represents a large multi-scenario forecasting benchmark. The challenge consists on predicting 3161 human trajectories, observing for each trajectory 8 consecutive ground-truth values (3.2 seconds) i.e., t−7,t−6,…,t, in world plane coordinates (the so-called world plane Human-Human protocol) and forecasting the following 12 (4.8 seconds), i.e., t+1,…,t+12. The 8-12-value protocol is consistent with the most trajectory forecasting approaches, usually focused on the 5-dataset ETH-univ + ETH-hotel + UCY-zara01 + UCY-zara02 + UCY-univ. Trajnet extends substantially the 5-dataset scenario by diversifying the training data, thus stressing the flexibility and generalization one approach has to exhibit when it comes to unseen scenery/situations. In fact, TrajNet is a superset of diverse datasets that requires to train on four families of trajectories, namely 1) BIWI Hotel (orthogonal bird’s eye flight view, moving people), 2) Crowds UCY (3 datasets, tilted bird’s eye view, camera mounted on building or utility poles, moving people), 3) MOT PETS (multisensor, different human activities) and 4) Stanford Drone Dataset (8 scenes, high orthogonal bird’s eye flight view, different agents as people, cars etc. ), for a total of 11448 trajectories. Testing is requested on diverse partitions of BIWI Hotel, Crowds UCY, Stanford Drone Dataset, and is evaluated by a specific server (ground-truth testing data is unavailable for applicants).