Dense small traffic object detection is essential for intelligent transportation systems but remains challenging because distant targets contain limited visual details, densely distributed objects frequently overlap, and complex road backgrounds introduce substantial interference. To address these limitations, this study proposes GAD-YOLO, a multi-level feature enhancement network based on YOLOv9. Ghost-MSConv performs lightweight multi-receptive-field feature extraction in the backbone, Mixed Local Channel Attention combines local spatial relationships with global channel dependencies during feature refinement, and DySample performs content-adaptive point sampling during feature upsampling. In the primary experiments on a six-class traffic object dataset derived from UA-DETRAC, GAD-YOLO achieved a precision of 78.9%, a recall of 76.4%, an ${\mathrm{mAP}}_{50}$ of 82.8%, and an ${\mathrm{mAP}}_{50:95}$ of 65.5%. Compared with YOLOv9c, precision, recall, ${\mathrm{mAP}}_{50}$, and ${\mathrm{mAP}}_{50:95}$ increased by 5.4, 0.5, 3.1, and 4.8 percentage points, respectively. Under the complexity statistics used in the primary experiments, GAD-YOLO contains 25.455 M parameters and requires 102.4 GFLOPs, compared with 25.442 M parameters and 103.2 GFLOPs for YOLOv9c. Additional experiments on the public VisDrone2019-DET benchmark were conducted to evaluate cross-dataset applicability, small-object performance, scene-density sensitivity, and standardized inference efficiency. On the VisDrone2019-DET test-dev set, GAD-YOLO improved ${\mathrm{mAP}}_{50}$ and ${\mathrm{mAP}}_{50:95}$ from 26.5% and 15.7% to 27.1% and 16.3%, respectively. A COCO-style analysis further showed that ${\mathrm{AP}}_S$ increased from 6.72% to 7.31%, while the dense-subset ${\mathrm{mAP}}_{50:95}$ increased from 13.95% to 14.44%. Under an RTX 4090, batch-size-one, $640\times 640$, FP32 inference protocol, GAD-YOLO achieved a mean latency of 9.98 ms and a throughput of 100.20 FPS. These results show that GAD-YOLO improves the primary traffic object detection task and yields modest positive performance differences on an independent public benchmark under the fixed experimental setting, while maintaining real-time inference capability.

Fine-grained poisonous mushroom recognition in natural scenes is challenging because of complex backgrounds, subtle morphological differences, and the limited interpretability of model decisions. To address these challenges, this paper proposes Att-FiLM, an attention-enhanced multi-scale Feature-Wise Linear Modulation network for poisonous mushroom image recognition. The model adopts an asymmetric dual-backbone architecture in which a frozen ConvNeXt-Base branch provides global semantic priors, while a trainable EfficientNet-B0 branch learns local discriminative features. Rather than directly concatenating heterogeneous features, Att-FiLM generates scale and shift parameters from semantic features and performs channel-wise modulation on multi-scale EfficientNet features at Stage 2 and Stage 4. This mechanism enables global semantic information to guide local feature learning while reducing feature redundancy and semantic inconsistency. Experimental results show that Att-FiLM achieves an Accuracy of 95.58% and an F1-score of 0.9455 on the poisonous/edible binary classification task. On the 190-class species-level classification task, it achieves a Top-1 Accuracy of 93.63% and a Macro-F1 of 0.9347. Interpretability analysis further shows that decision-relevant responses are frequently associated with morphologically relevant regions, including gills, annuli, volvae, and cap textures. These results indicate that Att-FiLM provides effective recognition performance together with interpretable decision evidence for mushroom recognition in complex natural scenes.

Software vulnerability mining is important for improving software reliability and security. Compared with sequence-based models, graph neural networks (GNNs) can explicitly model program structures such as syntax, control flow, data flow, call relations, and dependency paths, and have therefore been widely studied for vulnerability detection, localization, explanation, and repair. This paper presents a PRISMA-informed structured topical review of 87 studies and addresses five research questions concerning program graph representation, homogeneous and heterogeneous GNN architectures, Graph–LLM integration, evaluation reliability, and future research directions. The reviewed evidence shows that graph-based methods are most effective when vulnerability mechanisms can be faithfully represented through explicit structural relations. However, their reported performance remains strongly affected by duplicated samples, random function-level splits, noisy labels, incomplete repository context, graph-construction errors, and weak explanation protocols. Homogeneous GNNs provide efficient structural message passing but may mix different semantic relations, whereas heterogeneous GNNs preserve relation types more explicitly at the cost of greater graph-quality and computational requirements. Graph–LLM systems can improve semantic reasoning, repository-level analysis, explanation generation, and repair support, but their benefits should be evaluated together with memory consumption, inference latency, deployment complexity, and verification cost. This review further proposes minimum requirements for reliable vulnerability benchmarks and verifiable explanations, and develops a strategic agenda covering leakage-resistant datasets, uncertainty-aware graph construction, repository-level evaluation, cost-effective Graph–LLM collaboration, and graph-guided autonomous vulnerability repair.