Robust object detection methods in traffic surveillance scenarios often encounters challenges due to large-scale deformations and illumination variations in outdoor scenes. To enhance the tolerance of such methods against these variations, we design a cross-scale and illumination-invariant detection model (CSIM) based on the You Only Look Once (YOLO) architecture. A main cause of false detection in large-scale detection tasks is the inconsistency between various feature scales. To address this issue, we introduce an adaptive cross-scale feature fusion model to ensure the consistency of the constructed feature pyramid. To overcome the influence of uneven light, we build an illumination-invariant chromaticity space on the CSIM model, which is independent of the correlated color temperature. In addition, we adopt spatial attention modules, K-means clustering and the Mish activation function for further model optimization. The obtained experimental results show that the proposed CSIM produces excellent detection results for addressing the challenges derived from large-scale deformations and the illumination changes encountered during traffic surveillance. Compared with state-of-the-art object detection methods on public datasets, our proposed model has achieved competitive results in robust object detection tasks in traffic surveillance scenarios.

This paper presents an intelligent inspection system for power insulators, integrating Unmanned Aerial Vehicles (UAVs) to address complex conditions. A novel image enhancement technique, based on the multi-scale Retinex algorithm with combined illumination correction and compensation, is proposed to enhance both the overall image quality of insulators and the visual perception for human observers. The study employs the state-of-the-art YOLOv7 target detector for automated identification of insulator defects. The YOLOv7 model undergoes further optimization, enhancing the feature extraction structure, incorporating the SE attention mechanism, and refining the loss function. These modifications enable the model to adapt to more intricate intelligent inspection tasks related to transmission line insulators, resulting in improved efficiency and detection accuracy. Building on this research out-comes, our team introduces an intelligent power insulator detection software system tailored for UAV inspection scenarios

Feature correspondence is an essential issue in computer science, which could be well formulated by graph matching. However, traditional graph matching is susceptible to outliers, thus limits the applications. To address the issue, we present a biologically inspired feature descriptor (BIFD) corresponding to the simple and complex cell layers in primary visual cortex, which shows robust performance against deformations. Furthermore, we propose a novel biologically inspired structural model (BISM) for feature correspondence by fusing the graph structural information and appearance information described by BIFD in the images. The proposed BIFD imitates the cortical pooling operation across multi-scale and multi-angle cell layers, which makes BISM robust to outliers and distortions. To demonstrate the validity of the proposed method, we evaluate it in feature correspondence tasks on the public databases. Experiment results on synthetic data prove the validity of the proposed method.

Inductive power transfer (IPT) technology has the advantages of convenience and safety. The complete, reliable and excellent performance has been a hot research topic worldwide. Magnetic integration technology refers to the winding of two or more discrete magnetic elements in the same core to form a complete magnet. The magnetic integration is a promising process which integrates the magnetic elements in a single structure, allows the magnet loss, volume and net weight on a pair of cores to be reduced, thus increasing the portability and circuit performance. In this paper, we propose an IPT system with magnetic integration. Its double D round structure coils enable the absence of the compensation network at the receiver side and has an advantage on improving the misalignment performance on IPT system, meanwhile effectively reduces the weight and volume of the system, making miniaturization of the system possible and maintaining the power transmission capability.

The accuracy of object detection is crucial for the safety and efficiency of traffic management in monitoring systems. Existing detectors, however, struggle significantly within complex urban scenarios where high-density occlusions among the targets occur, as well as extreme scale variations resulting from the size differences of vehicles and distance variations to the camera. To remedy these issues, we introduce DFE-YOLO, an enhanced multi-scale detection framework built upon YOLOv8 that fuses features from various layers at different scales through our ‘four adaptive spatial feature fusion’ module, which performs adaptive spatial fusion via learnable weights normalized by softmax and thereby allows effective feature aggregation across scales. The second contribution is DySample, which uses a lightweight, content-aware, point-based upsampling method to improve multi-scale feature representation as well as reduce imbalance across different object scales. The experiments conducted on the VisDrone-2019 and BDD100K benchmarks showed significantly superior performance against state-of-the-art detectors. Specifically, DFE-YOLO achieved a +4% and +5.1% boost over YOLOv10 in AP and APsmall. This study offers a useful fix for smart transport systems.