Real-Time Object Detection: A Hybrid Framework Integrating Adaptive Local Differential Binary (LDB) and Deep Learning Architectures

Abstract

The new hybrid system for real-time object detection presented in this paper combines sophisticated deep learning architectures with improved Adaptive Local Differential Binary (LDB) features. Although deep learning models, especially one-stage detectors like the YOLO family, provide excellent speed and accuracy, they frequently need a large amount of processing power and can be sensitive to changes in the environment, such as noise or changes in illumination. Conventional local descriptors, such Local Binary Patterns (LBP) and its variations, are appropriate for localized pattern identification since they are computationally efficient and resistant to monotonic grayscale fluctuations. By adding adaptive thresholding, multi-scale processing, and rotation-invariance to the basic LDB approach, the suggested framework produces a strong Adaptive LDB feature extraction module. Through a multi-level fusion technique, these discriminative yet lightweight features are then easily combined with a cutting-edge deep learning backbone (such as a contemporary YOLO variation). According to preliminary analysis, this hybrid approach can achieve superior performance by maintaining efficient computational complexity (Floating-point Operations, FLOPs) while striking a balance between high detection accuracy (mean Average Precision, mAP) and real-time inference speed (frames per second, FPS, and latency). This work demonstrates how complementary strengths from deep learning and traditional learning paradigms can be combined to address important problems in resource-constrained, real-world object identification applications.