This paper proposes a lightweight and efficient mango detection model named Light-YOLO based on the Darknet53 structure, aiming to rapidly and accurately detect mango fruits in natural environments, effectively mitigating instances of false or missed detection. We incorporate the bidirectional connection module and skip connection module into the Darknet53 structure and compressed the number of channels of the neck, which minimizes the number of parameters and FLOPs. Moreover, we integrate structural heavy parameter technology into C2f, redesign the Bottleneck based on the principles of the residual structure, and introduce an EMA attention mechanism to amplify the network’s emphasis on pivotal features. Lastly, the Downsampling Block within the backbone network is modified, transitioning it from the CBS Block to a Multi-branch–Large-Kernel Downsampling Block. This modification aims to enhance the network’s receptive field, thereby further improving its detection performance. Based on the experimental results, it achieves a noteworthy mAP of 64.0% and an impressive mAP0.5 of 96.1% on the ACFR Mango dataset with parameters and FLOPs at only 1.96 M and 3.65 G. In comparison to advanced target detection models like YOLOv5, YOLOv6, YOLOv7, and YOLOv8, it achieves improved detection outcomes while utilizing fewer parameters and FLOPs.

Image fusion is one of the most rapidly evolving fields in image processing today, and its applications are widely expanded in various fields. In the field of image fusion, the method based on multi-scale decomposition plays an important role. However, it faces many difficult puzzles, such as the risk of over-smoothing during decomposition, blurring of fusion results, and loss of details. Aiming at these problems, this paper proposes a novel decomposition-based image fusion framework, which overcomes the problems of noise, blurring, and loss of details. Both the symmetry and asymmetry between infrared and visible images are important research hotspots in this paper. The experiments confirmed that the fusion framework outperforms other methods in both subjective observation and objective evaluation.