Fish object detection has attracted significant attention because of the considerable role that fish play in human society and ecosystems and the necessity to gather more comprehensive fish data through underwater videos or images. However, fish detection has always faced difficulties with the occlusion problem because of dense populations and underwater plants that obscure them, and no perfect solution has been found until now. To address the occlusion issue in fish detection, the following effort was made: creating a dataset of occluded fishes, integrating the innovative modules in Real-time Detection Transformer (RT-DETR) into You Only Look Once v8 (YOLOv8), and applying repulsion loss. The results show that in the occlusion dataset, the mAP of the original YOLOv8 is 0.912, while the mAP of our modified YOLOv8 is 0.971. In addition, our modified YOLOv8 also has better performance than the original YOLOv8 in terms of loss curves, F1–Confidence curves, P–R curves, the mAP curve and the actual detection effects. All these indicate that our modified YOLOv8 is suitable for fish detection in occlusion scenes.

Public safety and intelligent surveillance systems rely on anomaly detection for effective monitoring. In real-world pedestrian detection scenarios, Pedestrians often exhibit various symmetrical features such as body contours, facial features, posture, and clothing. However, the accuracy of pedestrian anomaly detection is affected by factors such as complex backgrounds, pedestrian obstruction, and small target sizes. To address these issues, this study introduced YOLO-ABD, a lightweight method for anomaly behavior detection that integrated small object detection and channel shuffling. This approach enhanced the YOLOv8n baseline model by integrating a small-object detection mechanism at the head and employing the symmetric GSConv convolutional module in the backbone network to improve perceptual capabilities. Furthermore, it incorporated the SimAM attention mechanism to mitigate complex background interference and thus enhance target detection performance. Evaluation on the IITB-Corridor dataset showed mAP50 and mAP50-95 scores of 89.3% and 60.6%, respectively. Generalization testing on the street-view-gdogo dataset further underscored the superiority of YOLO-ABD over advanced detection algorithms, demonstrating its effectiveness and generalization capabilities. With relatively fewer parameters, YOLO-ABD provided an excellent lightweight solution for pedestrian anomaly detection.

Due to the complexity of network penetration and the diversity of penetration methods, traditional analysis approaches analyse only a single penetration method or part of the network penetration process. Moreover, the lack of customized exploration makes it difficult to discover and analyse network penetration behaviors. Characterizing and summarizing the penetration testing process based on an interpretive visual analysis approach can enhance researchers’ comprehension of penetration testing and further promote the development of network security technologies. To assist with this process, we design PTVis, a visual approach for the penetration testing process summarization based on visual narrative and auxiliary decision. PTVis consists of two primary components: (1) a visual interface that displays customized penetration testing paths, and (2) a component that effectively displays the results of penetration testing. To design PTVis, penetration testing paths that combine penetration testing methods and tools are built via cooperative multi-view and customized exploration, which facilitates the exploration of penetration testing. For evaluation, a qualitative user study is performed on two groups. The feedback from the study demonstrates that PTVis can enhance the user’s knowledge of the penetration testing process.