Lihua Chen

Small object detection, which is frequently applied in defect detection, medical imaging, and security surveillance, often suffers from low accuracy due to limited feature information and blurred details. This paper proposes a small object detection method named YOLO-DHGC, which employs a two-stream structure with dense connections. Firstly, a novel backbone network, DenseHRNet, is introduced. It innovatively combines a dense connection mechanism with high-resolution feature map branches, effectively enhancing feature reuse and cross-layer fusion, thereby obtaining high-level semantic information from the image. Secondly, a two-stream structure based on an edge-gated branch is designed. It uses higher-level information from the regular detection stream to eliminate irrelevant interference remaining in the early processing stages of the edge-gated stream, allowing it to focus on processing information related to shape boundaries and accurately capture the morphological features of small objects. To assess the effectiveness of the proposed YOLO-DHGC method, we conducted experiments on several public datasets and a self-constructed dataset. Exceptionally, a defect detection accuracy of 96.3% was achieved on the Market-PCB public dataset, demonstrating the effectiveness of our method in detecting small object defects for industrial applications.

In the scenario of power system monitoring, detecting the operating status of circuit breakers is often inaccurate due to variable object scales and background interference. This paper introduces DLCH-YOLO, an object detection algorithm aimed at identifying the operating status of circuit breakers. Firstly, we propose a novel C2f_DLKA module based on Deformable Large Kernel Attention. This module adapts to objects of varying scales within a large receptive field, thereby more effectively extracting multi-scale features. Secondly, we propose a Semantic Screening Feature Pyramid Network designed to fuse multi-scale features. By filtering low-level semantic information, it effectively suppresses background interference to enhance localization accuracy. Finally, the feature extraction network incorporates Generalized-Sparse Convolution, which combines depth-wise separable convolution and channel mixing operations, reducing computational load. The DLCH-YOLO algorithm achieved a 91.8% mAP on our self-built power equipment dataset, representing a 4.7% improvement over the baseline network Yolov8. With its superior detection accuracy and real-time performance, DLCH-YOLO outperforms mainstream detection algorithms. This algorithm provides an efficient and viable solution for circuit breaker status detection.

Grounded Visual Question Answering systems place heavy reliance on substantial computational power and data resources in pretraining. In response to this challenge, this paper introduces the LCV2 modular approach, which utilizes a frozen large language model (LLM) to bridge the off-the-shelf generic visual question answering (VQA) module with a generic visual grounding (VG) module. It leverages the generalizable knowledge of these expert models, avoiding the need for any large-scale pretraining. Innovatively, within the LCV2 framework, question and predicted answer pairs are transformed into descriptive and referring captions, enhancing the clarity of the visual cues directed by the question text for the VG module’s grounding. This compensates for the limitations of missing intrinsic text–visual coupling in non-end-to-end frameworks. Comprehensive experiments on benchmark datasets, such as GQA, CLEVR, and VizWiz-VQA-Grounding, were conducted to evaluate the method’s performance and compare it with several baseline methods. In particular, it achieved an IoU F1 score of 59.6% on the GQA dataset and an IoU F1 score of 37.4% on the CLEVR dataset, surpassing some baseline results and demonstrating the LCV2’s competitive performance.