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Ferrante Neri

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3 papers

1 author row

EAAI Journal 2026 Journal Article

Application of a dynamic object detector with adaptive adjustment based on image complexity in the detection of drone aerial images

Ferrante Neri
Zehua Zhang
Yu Xue
Márcio P. Basgalupp

The detection of drone aerial images has become a research hotspot due to the extensive application of drones in various fields. However, the presence of a large number of small objects and complex scenes in the images poses severe challenges to the detection task. As an emerging technology, dynamic neural networks, with their input adaptive adjustment mechanism, provide important theoretical and technical support for solving the problem of detecting small objects in complex scenes of drone aerial images. In this work, we propose Dynamic You Only Look Once Object Detector (Dynamic-YOLO). Firstly, we improve You only look once (YOLO) v9. By leveraging the Convolutional Block Attention Module (CBAM) attention mechanism and Space-to-Depth Convolution (SPD-Conv), we optimise its backbone network to enhance the model’s feature extraction ability. Then, we adjust the structure of its neck network, delete the detection head P5 for large objects, and add the detection head P2 for tiny objects. After that, we combine a dynamic router with the improved YOLOv9 to form a dynamic detector, achieving adaptive processing of images. The experimental results on the Vision Meets Drone (VisDrone) benchmark dataset have demonstrated the excellent performance and flexibility of Dynamic-YOLO. When the Floating Point Operations (FLOPs) increase to 101. 5 G, the highest mean Average Precision at Intersection over Union 0. 5: 0. 95 (mAP@0. 5: 0. 95) reaches 32. 3% and mAP@0. 5 hits 51%, representing a 4. 0% and 10% improvement over YOLOv9-M. In addition, by dynamically adjusting the parameters of the router, the computational resource consumption and detection accuracy of the model can be flexibly adjusted, effectively achieving an optimised balance between detection performance and resource costs.

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EAAI Journal 2023 Journal Article

Continuously evolving dropout with multi-objective evolutionary optimisation

Pengcheng Jiang
Yu Xue
Ferrante Neri

Dropout is an effective method of mitigating over-fitting while training deep neural networks (DNNs). This method consists of switching off (dropping) some of the neurons of the DNN and training it by keeping the remaining neurons active. This approach makes the DNN general and resilient to changes in its inputs. However, the probability of a neuron belonging to a layer to be dropped, the ’dropout rate’, is a hard-to-tune parameter that affects the performance of the trained model. Moreover, there is no reason, besides being more practical during parameter tuning, why the dropout rate should be the same for all neurons across a layer. This paper proposes a novel method to guide the dropout rate based on an evolutionary algorithm. In contrast to previous studies, we associate a dropout with each individual neuron of the network, thus allowing more flexibility in the training phase. The vector encoding the dropouts for the entire network is interpreted as the candidate solution of a bi-objective optimisation problem, where the first objective is the error reduction due to a set of dropout rates for a given data batch, while the second objective is the distance of the used dropout rates from a pre-arranged constant. The second objective is used to control the dropout rates and prevent them from becoming too small, hence ineffective; or too large, thereby dropping a too-large portion of the network. Experimental results show that the proposed method, namely GADropout, produces DNNs that consistently outperform DNNs designed by other dropout methods, some of them being modern advanced dropout methods representing the state-of-the-art. GADroput has been tested on multiple datasets and network architectures.

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IJCAI Conference 2023 Conference Paper

Independent Feature Decomposition and Instance Alignment for Unsupervised Domain Adaptation

Qichen He
Siying Xiao
Mao Ye
Xiatian Zhu
Ferrante Neri
Dongde Hou

Existing Unsupervised Domain Adaptation (UDA) methods typically attempt to perform knowledge transfer in a domain-invariant space explicitly or implicitly. In practice, however, the obtained features is often mixed with domain-specific information which causes performance degradation. To overcome this fundamental limitation, this article presents a novel independent feature decomposition and instance alignment method (IndUDA in short). Specifically, based on an invertible flow, we project the base features into a decomposed latent space with domain-invariant and domain-specific dimensions. To drive semantic decomposition independently, we then swap the domain-invariant part across source and target domain samples with the same category and require their inverted features are consistent in class-level with the original features. By treating domain-specific information as noise, we replace it by Gaussian noise and further regularize source model training by instance alignment, i. e. , requiring the base features close to the corresponding reconstructed features, respectively. Extensive experiment results demonstrate that our method achieves state-of-the-art performance on popular UDA benchmarks. The appendix and code are available at https: //github. com/ayombeach/IndUDA.

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