Real-Time Flying Object Detection with YOLOv8 (2305.09972v2)

Abstract: This paper presents a generalized model for real-time detection of flying objects that can be used for transfer learning and further research, as well as a refined model that achieves state-of-the-art results for flying object detection. We achieve this by training our first (generalized) model on a data set containing 40 different classes of flying objects, forcing the model to extract abstract feature representations. We then perform transfer learning with these learned parameters on a data set more representative of real world environments (i.e. higher frequency of occlusion, very small spatial sizes, rotations, etc.) to generate our refined model. Object detection of flying objects remains challenging due to large variances of object spatial sizes/aspect ratios, rate of speed, occlusion, and clustered backgrounds. To address some of the presented challenges while simultaneously maximizing performance, we utilize the current state-of-the-art single-shot detector, YOLOv8, in an attempt to find the best trade-off between inference speed and mean average precision (mAP). While YOLOv8 is being regarded as the new state-of-the-art, an official paper has not been released as of yet. Thus, we provide an in-depth explanation of the new architecture and functionality that YOLOv8 has adapted. Our final generalized model achieves a mAP50 of 79.2%, mAP50-95 of 68.5%, and an average inference speed of 50 frames per second (fps) on 1080p videos. Our final refined model maintains this inference speed and achieves an improved mAP50 of 99.1% and mAP50-95 of 83.5%

• The paper presents a novel YOLOv8 detection model that achieves 0.835 mAP50-95 and 50 FPS on 1080p video streams.
• The paper leverages transfer learning and a diversified dataset of 40 flying object classes to enhance detection robustness under challenging conditions.
• The paper demonstrates significant practical implications for surveillance and defense by offering a transferable baseline for real-world security applications.

Real-Time Flying Object Detection with YOLOv8

In the paper titled "Real-Time Flying Object Detection with YOLOv8," the authors present an innovative approach to addressing the complex challenge of flying object detection through the lens of transfer learning and single-shot architecture. The focus of this work is to develop a generalized model capable of real-time detection, which can also serve as a baseline for further research and as a refined model ready for immediate implementation. Utilizing the YOLOv8 architecture, the authors achieve a meticulous balance between rapid inference and mean average precision (mAP), specifically targeting mAP50-95.

Key Methodologies and Results

The development process began with training a generalized model on a diversified dataset comprising 40 classes of flying objects. This diversified data approach was intended to coax the model toward learning abstract feature representations across varying conditions. Subsequently, the learned parameters were subjected to transfer learning on a dataset more reflective of real-world complexity, such as occlusions, rotations, and varying object sizes. The final refined model demonstrated significant proficiency, achieving an mAP50-95 of 0.835 while sustaining an inference speed of 50 frames per second on 1080p video streams.

The decision to leverage YOLOv8, despite its lack of an accompanying formal research paper at the time, was predicated on its recognized superiority in balancing accuracy and processing speed. Notably, the authors conducted an in-depth analysis of YOLOv8's architectural innovations, including its combination of Feature Pyramid Network (FPN) and Path Aggregation Network (PAN), alongside advanced approaches to labeling and model simplification, which collectively contribute to its efficiency.

Empirical Approach

The experiments addressed several technical hurdles inherent in flying object detection, notably class imbalance, variance in spatial distributions, and objects with low electromagnetic signatures like drones. A sequential approach was employed, beginning with selecting the appropriate YOLOv8 model size based on evaluation metrics and a hyper-parameter grid search under constrained computational resources. Furthermore, the integration of transfer learning techniques underscored the model's adaptability to new data distributions, initially trained with weights derived from a model grounded in the COCO dataset.

Theoretical and Practical Implications

The outcomes achieved indicate a pragmatic solution to a pressing security challenge, potentially benefitting applications such as border surveillance and infrastructure defense against malicious drone activity. The adaptability of the generalized model ensures its utility in diverse environments, fostering a viable tool for both theoretical exploration and immediate pragmatic deployment. Furthermore, the detailed exploration of YOLOv8's architecture presented in the paper enriches the discourse surrounding state-of-the-art object detection methodologies.

Future Directions

While the paper delineates a compelling case for YOLOv8 in real-time flying object detection, opportunities remain for enhancing model robustness across a broader spectrum of environmental conditions and object classes. An exploration into the integration of additional modality inputs, such as thermal imaging, could extend the operational framework of the proposed detection system. Additionally, further refinements related to post-processing algorithms and real-world deployment in distributed and network-constrained environments warrant scholarly investigation.

In conclusion, the paper provides a comprehensive examination of the application of YOLOv8 in the dynamic field of flying object detection, offering both a sound technical methodology and an avenue for future research to further optimize real-time recognition systems within complex visual contexts.