In order to solve the manual labelling, long-tail effect and driving conservatism of the existing decision-making algorithm. This paper proposed an integrated decision-making framework (IDF) for highway autonomous vehicles. Firstly, states of the highway traffic are extracted by the velocity, time headway (TH) and the probabilistic lane distribution of the surrounding vehicles. With the extracted traffic state, the reinforcement learning (RL) is adopted to learn the optimal state-action pair for specific scenario. Analogously, by mapping millions of traffic scenarios, huge amounts of state-action pairs can be stored in the experience pool. Then the imitation learning (IL) is further employed to memorize the experience pool by deep neural networks. The learning result shows that the accuracy of the decision network can reach 94.17%. Besides, for some imperfect decisions of the network, the rule-based method is taken to rectify by judging the long-term reward. Finally, the IDF is simulated in G25 highway and has promising results, which can always drive the vehicle to the state with high efficiency while ensuring safety.

In recent years, tragedies caused by forest fires have been frequently reported. Forest fires not only result in significant economic losses but also cause environmental damage. The utilization of computer vision techniques and unmanned aerial vehicles (UAVs) for forest fire monitoring has become a primary approach to accurately locate and extinguish fires during their early stages. However, traditional computer-based methods for UAV forest fire image segmentation require a large amount of pixel-level labeled data to train the networks, which can be time-consuming and costly to acquire. To address this challenge, we propose a novel weakly supervised approach for semantic segmentation of fire images in this study. Our method utilizes self-supervised attention foreground-aware pooling (SAP) and context-aware loss (CAL) to generate high-quality pseudo-labels, serving as substitutes for manual annotation. SAP collaborates with bounding box and class activation mapping (CAM) to generate a background attention map, which aids in the generation of accurate pseudo-labels. CAL further improves the quality of the pseudo-labels by incorporating contextual information related to the target objects, effectively reducing environmental noise. We conducted experiments on two publicly available UAV forest fire datasets: the Corsican dataset and the Flame dataset. Our proposed method achieved impressive results, with IoU values of 81.23% and 76.43% for the Corsican dataset and the Flame dataset, respectively. These results significantly outperform the latest weakly supervised semantic segmentation (WSSS) networks on forest fire datasets.

Counting the soybean pods automatically has been one of the key ways to realize intelligent soybean breeding in modern smart agriculture. However, the pod counting accuracy for whole soybean plants is still limited due to the crowding and uneven distribution of pods. In this paper, based on the VFNet detector, we propose a deformable attention recursive feature pyramid network for soybean pod counting (DARFP-SD), which aims to identify the number of soybean pods accurately. Specifically, to improve the feature quality, DARFP-SD first introduces the deformable convolutional networks (DCN) and attention recursive feature pyramid (ARFP) to reduce noise interference during feature learning. DARFP-SD further combines the Repulsion Loss to correct the error of predicted bboxse coming from the mutual interference between dense pods. DARFP-SD also designs a density prediction branch in the post-processing stage, which learns an adaptive soft distance IoU to assign suitable NMS threshold for different counting scenes with uneven soybean pod distributions. The model is trained on a dense soybean dataset with more than 5300 pods from three different shapes and two classes, which consists of a training set of 138 images, a validation set of 46 images and a test set of 46 images. Extensive experiments have verified the performance of proposed DARFP-SD. The final training loss is 1.281, and an average accuracy of 90.35%, an average recall of 85.59% and a F1 score of 87.90% can be achieved, outperforming the baseline method VFNet by 8.36%, 4.55% and 7.81%, respectively. We also validate the application effect for different numbers of soybean pods and differnt shapes of soybean. All the results show the effectiveness of the DARFP-SD, which can provide a new insight into the soybean pod counting task.