Deep Learning-Driven Target Recognition for Robotic Weaponry Systems: A Neural Network-Based Approach
Kartikey Shukla, Amar Deep Gupta, Manjeet Kaur Ratan, Mohammed Zakariah · 2026
Robotic weapons systems have been significantly improved in target recognition abilities through the integration of neural networks and deep learning strategies. Rule-based models and conventional machine learning models such as SVM and k-NN often tend to be of low precision in complex scenarios, possess limited flexibility, and are inefficient with real-time data. Such limitations render autonomous robotic systems less effective in aggressive and dynamic environments. We propose the Deep Target Recognition and Response System (DTRRS), a deep learning architecture fusing Recurrent Neural Networks (RNNs) for temporal pattern extraction and Convolutional Neural Networks (CNNs) for spatial feature extraction, to counter such challenges. LiDAR, thermal imagery, and visual streams are some of the multi-modal sensor information that are integrated into the system. Detection and classification performance are enhanced through the emphasis on threat-bearing features by adding an attention mechanism. Real-time collection of sensor data, adaptive filtering preprocessing, and classification by the learned deep network are all included in the workflow of the DTRRS. Online learning allows a feedback loop to enhance predictions as the system is being used. It is suitable for autonomous deployment in military situations because of its real-time adaptability. DTRRS performs better than traditional CNN-alone models (89.7%), SVM-learner-based classifiers (83.5%), and rule-based systems (76.2%) with a target recognition rate of 96.3%, as per experimental tests on a war-simulated dataset and a real-world battlefield imagery benchmark. The model also demonstrates increased confidence in classification and reduced false detection rates when subjected to occlusion and variable lighting. These results validate the proposed system&s;s effectiveness for use in future intelligent robotic weapon systems.