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To prove that a certain algorithm is superior to the benchmark algorithms, the statistical hypothesis tests are commonly adopted with experimental results on a number of datasets. Some statistical hypothesis tests draw statistical test results more conservative than the others, while it is not yet possible to characterize quantitatively the degree of conservativeness of such a statistical test. On the basis of the existing nonparametric statistical tests, this paper proposes a new statistical test for multiple comparison which is named as t-Friedman test. T-Friedman test combines t test with Friedman test for multiple comparison. The confidence level of the t test is adopted as a measure of conservativeness of the proposed t-Friedman test. A bigger confidence level infers a higher degree of conservativeness, and vice versa. Based on the synthetic results generated by Monte Carlo simulations with predefined distributions, the performance of several state-of-the-art multiple comparison tests and post hoc procedures are first qualitatively analyzed. The influences of the type of predefined distribution, the number of benchmark algorithms and the number of datasets are explored in the experiments. The conservativeness measure of the proposed method is also validated and verified in the experiments. Finally, some suggestions for the application of these nonparametric statistical tests are provided.

The identification and positioning of flying projectiles is a critical issue that affects the testing accuracy of equipment in ballistic testing technology. Traditional image processing methods are difficult to accurately extract targets due to the characteristics of small target size, fast speed, and strong fragmentation interference of projectiles ejected from the muzzle. This paper proposes a projectile recognition algorithm based on an improved YOLOX detection model for the detection and recognition of flying projectiles. The fast and accurate YOLOX model is used, and the network structure is improved by adding attention mechanisms in the feature fusion module to enhance the detection ability of small targets; the loss function is also improved to enhance the model’s iterative optimization ability. Test results show that the improved YOLOX model has significantly improved accuracy in projectile recognition compared to the original network, reaching 84.82%, demonstrating the feasibility of the proposed approach. The improved algorithm can be effectively used for small target scenarios in range testing and significantly improves the accuracy of recognition.

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