Deficiencies in road anomaly detection systems precipitate multifaceted risks, including elevated collision probabilities from unidentified hazards, compromised traffic flow efficiency, and exponential maintenance costs. Contemporary methods struggle with complex road environments, dynamic viewing perspectives, and limited datasets. We present AVD-YOLO, an enhanced YOLO variant that synergistically integrates Active Vision-Driven (AVD) multi-scale feature extraction with Position Modulated Attention (PMA) mechanisms. PMA addresses diminished target-background discriminability under variable illumination and weather conditions by capturing long range spatial dependencies, enhancing weak-feature target detection. The AVD technique mitigates missed detections caused by real-time viewing distance variations through adaptive multi-receptive field mechanisms, maintaining conceptual target fixation while dynamically adjusting feature scales. To address data scarcity, a comprehensive Multi-Class Road Anomaly Dataset (MCRAD) comprising 14,208 annotated images across nine anomaly categories is constructed. Experiments demonstrate that AVD-YOLO improves detection accuracy, achieving a 1.6% gain in mAP@0.5 and a 2.9% improvement in F1-score over baseline. These performance gains indicate both more precise localization of abnormal objects and a better balance between precision and recall, thereby enhancing the overall robustness of the detection model.

Vibration signals collected from harsh environments often suffer from severe noise interference, which can submerge critical fault characteristics. To address this challenge, a frequency attention network based on multi-scale parallel ResNet (MSPResNet-FA) is proposed. The architecture integrates a Fast Fourier Transform Convolution (FFT-Conv) module to extract harmonic frequency impulse feature, while parallel multi-scale kernels in the convolutional layer are employed to extract various scale local frequency features. Furthermore, a Convolutional Block Attention Module (CBAM) is utilized to enable the network to focus on and extract important features from the frequency domain especially the multi-scale harmonic frequency impulse feature. The effectiveness of MSPResNet-FA was validated using four benchmark datasets: Politecnico di Torino (PDT), Xi’an Jiao Tong University (XJTU), Paderborn University (PU), and the Society for Machinery Failure Prevention Technology (MFPT). Experimental results indicate that MSPResNet-FA outperforms current state-of-the-art methods, notably achieving accuracy gains of over 5% on the XJTU, MFPT, and PDT datasets. Ablation studies confirm that the FFT component is foundational to this high performance, contributing an improvement of over 10%, while the CBAM refinement layer provides an additional 1% enhancement.