Port underwater inspection is essential for ensuring maritime safety and operational continuity. However, low visibility and complex environments make it challenging to achieve reliable detection. This study addresses the issue of underwater target recognition based on multibeam sonar imagery. Specifically, we propose YOLO-ShuffleNet-BiFPN-EIOU, an enhanced YOLOv5 detection framework with three key improvements: first, the adoption of the lightweight backbone network ShuffleNetv2, second, the incorporation of a bidirectional feature pyramid network, and third, the optimization of the enhanced intersection over union loss function. Furthermore, we theoretically derive the maximum permissible velocity threshold for autonomous underwater vehicle (AUV), leading to a novel velocity-constrained controller that improves sonar imaging quality. A general transformation function is applied, and a functional dependence with quasi-linear characteristics between the independent and dependent variables is established, converting the partially constrained AUV system into an unconstrained one. Finally, experiments conducted in a pool and a real-world port demonstrate that the proposed method achieves significant improvements in accuracy and efficiency compared to YOLOv5m, with [email protected] increasing by 3.4%, [email protected]:0.95 improving by 5.3%, giga floating-point operations per second reduced by 89.8%, and the velocity-constrained AUV operation effectively enhances detection performance.

In visual SLAM (VSLAM) systems, loop closure plays a crucial role in reducing accumulated errors. However, VSLAM systems relying on low-level visual features often suffer from the problem of perceptual confusion in repetitive environments, where scenes in different locations are incorrectly identified as the same. Existing work has attempted to introduce object-level features or artificial landmarks. The former approach struggles to distinguish visually similar but different objects, while the latter is both time-consuming and labor-intensive. This paper introduces a novel loop closure detection method that leverages pretrained AI foundation models to extract rich semantic information about specific types of objects (e.g., door numbers), referred to as semantic anchors, that help to distinguish similar scenes better. In settings such as office buildings, hotels, and warehouses, this approach helps to improve the robustness of loop closure detection. We validate the effectiveness of our method through experiments conducted in both simulated and real-world environments.

Rust is a young systems programming language that is type and memory safe. Despite Rust’s design focus on safety and correctness, bugs are inevitable in any software system, and Rust is no exception. By analyzing 10097 bug reports and 9360 related revisions in the Rust language, we found that the bugs are extremely unevenly distributed in components and source files; most of the general language features involved in Rust language bugs are Data types, Expressions and Assignment Statements, Rust-specific language features are mainly related to Traits and Ownership Systems; the main symptom of bugs is Internal Compilation Error (ICE); The bug fix work is not complicated; there is a significant correlation between priority and fix time; Semantic bugs are the most common root cause of bugs. These findings reveal some basic patterns of bugs in the Rust language, which can provide some help to Rust developers and maintainers to improve the quality of the Rust language and provide a better programming experience for users of Rust language.

Swift programming language has been widely used in IOS application development and has formed a perfect Apple development ecosystem due to its syntactic simplicity and functionality. However, as a complex programming language, Swift inevitably has problems, which may cause the program to fail to run normally. In this paper, we empirically analyze the ones in the Swift language. We collected 7446 bugs and 2749 revisions and manually analyzed the root causes of 180 bugs. We found that defects in Swift are unevenly distributed in components and source files; the test cases are small in size, and the complexity and workload of defect fixing are not significant; the symptoms of defects manifest themselves in various forms, but mainly in the form of Crash; and the root causes of defects are mostly semantic bugs. The conclusions drawn based on the findings are helpful for the development, testing, maintenance, and application of the Swift programming language.