Images captured from a drone’s perspective are significantly impacted in terms of target detection algorithm performance due to the notable differences in target scales and the presence of numerous small target objects lacking detailed information. This paper proposes a Remote Sensing Small Target Detector (CF-YOLO) based on the YOLOv11 model to address the challenges of small target detection. Firstly, addressing the issue of small target information loss that may arise from hierarchical convolutional structures, we conduct in-depth research on the Path Aggregation Network (PAN) and innovatively propose a Cross-Scale Feature Pyramid Network (CS-FPN). Secondly, to overcome the problems of positional information deviation and feature redundancy during multi-scale feature fusion, we design a Feature Recalibration Module (FRM) and a Sandwich Fusion Module. We advocate for initial feature fusion through the FRM module, followed by feature enhancement using the Sandwich module. Finally, we optimize and reconstruct the model using the RFAConv module and LSDECD detection head. Experiments show that on the public VisDrone dataset, TinyPerson dataset, and HIT-UAV dataset, CF-YOLO improves the mAP50 by 12.7%, 10.1%, and 3.5%, respectively, compared to the baseline model. Compared to other methods, CF-YOLO demonstrates superior performance.

Small object detection in UAV aerial images is challenging due to low contrast, complex backgrounds, and limited computational resources. Traditional methods struggle with high miss detection rates and poor localization accuracy caused by information loss, weak cross-layer feature interaction, and rigid detection heads. To address these issues, we propose LRDS-YOLO, a lightweight and efficient model tailored for UAV applications. The model incorporates a Light Adaptive-weight Downsampling (LAD) module to retain fine-grained small object features and reduce information loss. A Re-Calibration Feature Pyramid Network (Re-Calibration FPN) enhances multi-scale feature fusion using bidirectional interactions and resolution-aware hybrid attention. The SegNext Attention mechanism improves target focus while suppressing background noise, and the dynamic detection head (DyHead) optimizes multi-dimensional feature weighting for robust detection. Experiments show that LRDS-YOLO achieves 43.6% mAP50 on VisDrone2019, 11.4% higher than the baseline, with only 4.17M parameters and 24.1 GFLOPs, striking a balance between accuracy and efficiency. On the HIT-UAV infrared dataset, it reaches 84.5% mAP50, demonstrating strong generalization. With its lightweight design and high precision, LRDS-YOLO offers an effective real-time solution for UAV-based small object detection.

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.

Most existing small object detection methods rely on residual blocks to process deep feature maps. However, these residual blocks, composed of multiple large-kernel convolution layers, incur high computational costs and contain redundant information, which makes it difficult to improve detection performance for small objects. To address this, we designed an improved feature pyramid network called L Feature Pyramid Network (L-FPN), which optimizes the allocation of computational resources for small object detection by reconstructing the original FPN structure. Based on L-FPN, we further proposed a small object detector named BPD-YOLO. We introduce a Dual-phase Asymptotic Feature Fusion mechanism (DAFF), where the shallow and deep semantic features extracted from the backbone network are initially fused in parallel to mitigate the semantic gap. Subsequently, the intermediate semantic layers are progressively integrated, enabling effective fusion of both shallow and deep feature representations. Additionally, we designed the Deep Spatial Pyramid Fusion module (DSPF), which generates multi-scale feature representations as an alternative to conventional residual block stacking, thereby reducing computational overhead. In the shallow feature extraction stage, DSPF focuses on semantic integration and enhances the extraction of small object features. This strategy, which adaptively selects different modules based on the resolution of the feature maps, is referred to as the Decoupled feature Extraction-semantic Integration mechanism (DEI). Finally, we conducted extensive experiments and thorough evaluations on both the VisDrone and TinyPerson datasets. The results demonstrate that, on the VisDrone dataset, compared to the baseline model YOLOv8n + p2, our BPD-YOLO model with L-FPN achieves a 2.8% improvement in mAP50 and a 1.4% increase in mAP50-95. On the TinyPerson dataset, BPD-YOLO further demonstrates its superiority in high-resolution feature extraction, effectively enhancing detection accuracy while significantly reducing computational costs.

Object detection is a crucial component of the perception system in autonomous driving. However, the road scene presents a highly intricate environment where the visibility and characteristics of traffic targets are susceptible to attenuation and loss due to various complex road scenarios such as lighting conditions, weather conditions, time of day, background elements, and traffic density. Nevertheless, the current object detection network must exhibit more learning capabilities when detecting such targets. This also exacerbates the loss of features during the feature extraction and fusion process, significantly compromising the network’s detection performance on traffic targets. This paper presents a novel methodology by which to overcome the concerns above, namely HRYNet. Firstly, a dual fusion gradual pyramid structure (DFGPN) is introduced, which employs a two-stage gradient fusion strategy to enhance the generation of more comprehensive multi-scale high-level semantic information, strengthen the interconnection between non-adjacent feature layers, and reduce the information gap that exists between them. HRYNet introduces an anti-interference feature extraction module, the residual multi-head self-attention mechanism (RMA). RMA enhances the target information by implementing a characteristic channel weighting policy, thereby reducing background interference and improving the attention capability of the network. Finally, the detection performance of HRYNet was evaluated by utilizing three datasets: the horizontally collected dataset BDD1000K, the UAV high-altitude dataset Visdrone, and a custom dataset. Experimental results demonstrate that HRYNet achieves a higher mAP_0.5 compared with YOLOv8s on the three datasets, with increases of 10.8%, 16.7%, and 5.5%, respectively. To optimize HRYNet for mobile devices, this study presents Lightweight HRYNet (LHRYNet), which effectively reduces the number of model parameters by 2 million. The results demonstrate that LHRYNet outperforms YOLOv8s in terms of mAP_0.5, with improvements of 6.7%, 10.9%, and 2.5% observed on the three datasets, respectively.