Estimation of the sintering state has importance for clinker quality improvements and the safe operation of the rotary kiln. Class imbalanced thermal signals usually pose challenges in feature extraction and abnormal state recognition. In this paper, a novel framework that integrates prior knowledge and hidden information is developed for sintering state recognition in the class imbalance condition. For discriminative feature extraction of imbalanced data, a cascaded stack autoencoder (SAE) model is proposed to fuse our prior knowledge and hidden information. The model includes a feature extraction SAE and a deep fusion SAE: the former extracts hidden information from thermal signals, and the latter deeply fuses and compresses our prior knowledge and hidden information. For the class imbalance of sintering samples, we propose a data-dependent kernel modification optimal margin distribution machine (ddKMODM) as a sintering state recognition model. Modifying the original kernel function by a conformal function depending on the data distribution in kernel space, ddKMODM can change the local volume expansion coefficient of the feature space to eliminate the negative effects caused by imbalanced samples. Experiments on real data show that the proposed framework can balance the detection rate of each state in the class imbalanced condition, and its overall sintering state recognition accuracy exceeds 92%.

Vehicular infrared object detection is a key technology supporting autonomous driving systems to achieve all-weather environmental perception. However, infrared images inherently lack texture, resulting in blurred object contours. Additionally, deep network propagation severely erodes and loses feature information of distant tiny objects. To address the above issues, this study proposes a Full-Scale Feature Synergistic Perception Architecture for vehicular infrared object detection. This architecture first designs a Gradient-Informed Attention module, which initializes convolution kernels through physical gradient operators to inject geometric prior information into the network, enhancing the model’s perception capability of blurred object boundaries. Secondly, it constructs a Full-Scale Feature Pyramid containing a $P_2$ high-resolution feature layer to effectively recover the geometric detail features of distant tiny objects. Finally, it proposes a Scale-Aware Shared Head, which relies on a cross-scale parameter sharing mechanism to achieve extreme parameter compression, and simultaneously introduces deep semantic information to form strong constraints, suppressing noise interference in shallow features. Experimental results on the FLIR v2 and M3FD datasets show that the proposed architecture exhibits excellent detection performance. On FLIR v2, it raises mAP@50 to 64.06% (6.51% relative gain vs. YOLOv11) while maintaining 547 FPS inference speed, achieving an optimal accuracy–efficiency balance.