Image-based obstacle detection methods for the safe navigation of industrial unmanned aerial vehicles

Computer vision is becoming increasingly important for industrial unmanned aerial vehicles (UAVs) to do real-time object and obstacle recognition while they are engaged in autonomous navigation. Variable texture features of objects, on the other hand, frequently lead to feature disappearance, which in turn reduces the accuracy of detection. This work proposed a novel Texture-variant Obstacle Object Classification Model (TOOCM) for feature extraction and dynamic texture representation. By capturing small texture fluctuations in real-time situations, the primary goal is to enhance the accuracy of both obstacle detection and object classification. The TOOCM model incorporates an N-layer ResNet architecture designed to address the difficulties of disappearing features in an adaptable Manner through dynamic layer augmentation based on variations in texture and size. Reconstruction of each convolutional layer in the ResNet is performed on an as-needed basis, taking into consideration the amount of texture concentration in incoming picture regions. The emergence of identifiable texture regions will result in the identification of new objects, which will then trigger adaptive categorization and layer restructuring activities. TOOCM, in contrast to traditional fixed-layer deep models, offers layer-wise learning updates during the navigation of UAV, which guarantees constant performance in complicated settings. The results of the experiments show that TOOCM achieves a greater detection accuracy of 14.09%, enhanced precision of 14.53%, and a loss reduction of 14.17%, particularly in high-density obstacle settings. These results demonstrate that the suggested adaptive feature learning approach is effective.