Abstract
With the development of artificial intelligence technology, intelligent weapon systems that can automatically identify, lock on and strike targets have gradually appeared and can replace humans in executing simple decision-making commands. Target detection is a key part of intelligent weapons. At present, large-scale target detection has serious challenges such as long-tail data distributions, severe occlusion, and category ambiguity. The main detection algorithms only detect each independent area without considering the key semantic dependencies between objects. It has become a hot trend to apply deep learning to prior knowledge to form a model. This article uses both internal and external knowledge to instill a target detection system with human reasoning capabilities. Commonly used external embedded knowledge includes geometric relations, attributes, locations, etc. They have a common shortcoming in that they require large amounts of labeled data, and the integration costs are huge. The purpose of this article is to construct a general external prior knowledge module to guide network learning. By paying attention to the characteristics of each object in different semantic contexts, the characteristics of each object are adaptively enhanced, and the high-level semantics of all categories evolve on a global scale. Internal knowledge uses a convolutional attention module that can learn spatial and channel information at multiple scales. The experimental results show the superiority of our knowledge-YOLOv5. The proposed method achieved 1.7%, 2.2%, 1.1%, and 0.7% improvements over YOLOv5s, YOLOv5m, VOLOv51, and YOLOv5x, respectively, on the COCO data sets; and the proposed method also achieves a 0.9% improvement on the self-built data set. The trained lightweight model Knowledge-YOLOv5s is deployed on an NVIDIA Jetson TX2 through TensorRT acceleration, and the real-time detection frame is 20 ms, which meets the real-time detection requirements. This system can also be used as a module of an intelligent weapon system, which has certain referential significance for autonomous weapons and unmanned combat systems.
Highlights
The advancement and application of informatization and intelligent technology have caused profound changes in the form of modern warfare and the battlefield environment
The major military powers in the world are vigorously promoting the strategy of intelligent weapons, VOLUME XX, 2017
Deep learning technology has developed rapidly, and target detection algorithms have been upgraded from traditional algorithms based on manual features to detection technologies based on deep neural networks
Summary
The advancement and application of informatization and intelligent technology have caused profound changes in the form of modern warfare and the battlefield environment. Due to the application of advanced information technology, the modern combat model and battlefield environment have undergone fundamental changes. China attaches great importance to the development of artificial intelligence and accelerates drones and goallessness through the "military-civilian integration" strategy and the development of military fields such as unmanned combat platforms. The new generation of the Russian unmanned combat vehicle "Uranus-9" has been tested in the Syrian battlefield It can march independently and search for targets independently. The current state-of-the-art object detection methods identify each area separately and require high-quality feature representation of each area and sufficient labeled data for each category. This is not the case for large-scale detection problems. Channel attention and spatial attention are applied in turn to focus on important features and suppress unnecessary features
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