The counting of pineapple buds relies on target recognition in estimating pineapple yield using unmanned aerial vehicle (UAV) photography. This research proposes the SFHG-YOLO method, with YOLOv5s as the baseline, to address the practical needs of identifying small objects (pineapple buds) in UAV vision and the drawbacks of existing algorithms in terms of real-time performance and accuracy. Field pineapple buds are small objects that may be detected in high density using a lightweight network model. This model enhances spatial attention and adaptive context information fusion to increase detection accuracy and resilience. To construct the lightweight network model, the first step involves utilizing the coordinate attention module and MobileNetV3. Additionally, to fully leverage feature information across various levels and enhance perception skills for tiny objects, we developed both an enhanced spatial attention module and an adaptive context information fusion module. Experiments were conducted to validate the suggested algorithm’s performance in detecting small objects. The SFHG-YOLO model exhibited significant gains in assessment measures, achieving mAP@0.5 and mAP@0.5:0.95 improvements of $7.4\%$ and $31\%$, respectively, when compared to the baseline model YOLOv5s. Considering the model size and computational cost, the findings underscore the superior performance of the suggested technique in detecting high-density small items. This program offers a reliable detection approach for estimating pineapple yield by accurately identifying minute items.

Floating debris threatens aquatic ecosystems, yet detecting it in close-range aquatic remote sensing imagery remains difficult because small, irregular, or partially submerged objects resemble specular highlights and ripples. This study proposes FLNet, a YOLO11-L-based detector for water-surface monitoring from unmanned surface vehicles (USVs). FLNet redesigns the backbone, neck, and head with three components: a Dynamic Polarity-Aware Robustness Block that calibrates deep activations and models similar and opposing feature relations; an Adaptive Multi-Receptive Pyramid that repeatedly fuses three adjacent scales through multi-branch depthwise convolutions; and a Detail-Preserving Shared Head that applies one boundary-sensitive prediction trunk to all scales. On a custom five-category dataset of 12,237 images, FLNet achieved 85.93% macro-Precision, 85.08% macro-F1, and 88.77% mean average Precision at an intersection-over-union threshold of 0.5 (mAP@0.5), the highest single-run values among the evaluated detectors, with 24 M parameters and 83 GFLOPs. Across three random seeds, its macro-Precision and macro-F1 advantages over the strongest competitor, Hyper-YOLO, exceeded seed-to-seed variability, whereas its mAP@0.5 was statistically comparable to that of Hyper-YOLO and Hyper-YOLO retained a higher mAP@0.5:0.95. In separately trained benchmarks, FLNet achieved the highest F1-score on FloW-Img (80.26%) and the highest macro-F1 on MarineDebris-yolo (79.82%). With TensorRT half-precision (FP16) acceleration, detector-side inference reached 29.8, 111.2, and 56.4 frames per second on Jetson Xavier NX, AGX Orin, and Orin Nano Super, respectively. FLNet therefore offers a favorable accuracy–efficiency balance for close-range floating-debris detection.