#include "zf_common_headfile.h" #include <math.h> unsigned char Image_Use[LCDH][LCDW]; unsigned char Bin_Image[LCDH][LCDW]; uint16 Threshold; sint16 Center_offset1=0; sint16 Center_offset2=0; sint16 Center_offset3=0; sint16 youbianxian_location[LCDW]={0}; sint16 youbianxian_flag[LCDH]={0} ; sint16 zuobianxian_location[LCDW]={0}; sint16 zuobianxian_flag[LCDH]={0} ; sint16 end_point=0; sint16 start_point; sint16 centerline[LCDH]={0}; sint16 therowmiddlepoint[LCDH]={0}; //******** int16_t Center_offset; float deviation_demo=0; volatile int Ramp_Flag=0; void camera_use(void) { if(mt9v03x_finish_flag == 1) { Transfer_Camera(mt9v03x_image[0], Image_Use[0], MT9V03X_W*MT9V03X_H); Bin_Image_TWO(); Bin_Image_Filter(); edge_search(); Deviation_compute(); mt9v03x_finish_flag = 0; } } void Transfer_Camera(uint8 *p, uint8 *q, int16 pixel_num) { for(int16 i = 0; i < pixel_num; i++) *(q +i) = *(p +i); } void Bin_Image_TWO() { Threshold = GetOSTU(Image_Use); uint16 thre; for(uint8 y = 0; y < MT9V03X_H; y++) //这里考虑到了图像边缘的光照偏弱的问题 { for(uint8 x = 0; x < MT9V03X_W; x++) { thre = Threshold; if (Image_Use[y][x] >thre) //数值越大,显示的内容越多,较浅的图像也能显示出来 Bin_Image[y][x] = 255; //白 else Bin_Image[y][x] = 0; //黑 } } } short GetOSTU (unsigned char tmImage[LCDH][LCDW]) { signed short i, j; unsigned long Amount = 0; unsigned long PixelBack = 0; unsigned long PixelshortegralBack = 0; unsigned long Pixelshortegral = 0; signed long PixelshortegralFore = 0; signed long PixelFore = 0; float OmegaBack, OmegaFore, MicroBack, MicroFore, SigmaB, Sigma; signed short MinValue, MaxValue; signed short Threshold = 0; unsigned char HistoGram[256]; for (j = 0; j < 256; j++) HistoGram[j] = 0; for (j = 0; j < LCDH; j++) { for (i = 0; i < LCDW; i++) { HistoGram[tmImage[j][i]]++; } } for (MinValue = 0; MinValue < 256 && HistoGram[MinValue] == 0; MinValue++); for (MaxValue = 255; MaxValue > MinValue && HistoGram[MinValue] == 0; MaxValue--); if (MaxValue == MinValue) return MaxValue; if (MinValue + 1 == MaxValue) return MinValue; for (j = MinValue; j <= MaxValue; j++) Amount += HistoGram[j]; Pixelshortegral = 0; for (j = MinValue; j <= MaxValue; j++) { Pixelshortegral += HistoGram[j] * j; } SigmaB = -1; for (j = MinValue; j < MaxValue; j++) { PixelBack = PixelBack + HistoGram[j]; PixelFore = Amount - PixelBack; OmegaBack = (float) PixelBack / Amount; OmegaFore = (float) PixelFore / Amount; PixelshortegralBack += HistoGram[j] * j; PixelshortegralFore = Pixelshortegral - PixelshortegralBack; MicroBack = (float) PixelshortegralBack / PixelBack; MicroFore = (float) PixelshortegralFore / PixelFore; Sigma = OmegaBack * OmegaFore * (MicroBack - MicroFore) * (MicroBack - MicroFore); if (Sigma > SigmaB) { SigmaB = Sigma; Threshold = j; } } return Threshold; } void Bin_Image_Filter (void) { sint16 nr; sint16 nc; for (nr = 0; nr < LCDH - 1; nr++) { for (nc = 1; nc < LCDW - 1; nc = nc + 1) { if ((Bin_Image[nr][nc] == 0) && (Bin_Image[nr - 1][nc] + Bin_Image[nr + 1][nc] + Bin_Image[nr][nc + 1] + Bin_Image[nr][nc - 1] > 2)) { Bin_Image[nr][nc] = 1; } else if ((Bin_Image[nr][nc] == 1) && (Bin_Image[nr - 1][nc] + Bin_Image[nr + 1][nc] + Bin_Image[nr][nc + 1] + Bin_Image[nr][nc - 1] < 2)) { Bin_Image[nr][nc] = 0; } } } } int STOP=0; void edge_search(void) { sint16 n,m,middleline = LCDW/2; end_point=0; for(n =LCDH-1; n >=LCDH-5 ; n--) { if(Bin_Image[n][LCDW/2]!=0) { for(m = middleline; m >= 1; m --) { if(Bin_Image[n][m] == 0) { zuobianxian_location[n] = m; zuobianxian_flag[n] = 1; break; } else { zuobianxian_location[n] = 1; zuobianxian_flag[n] = 0; } } for(m = middleline; m <= LCDW - 1; m ++) { if(Bin_Image[n][m] == 0) { youbianxian_location[n] = m; youbianxian_flag[n] = 1; break; } else { youbianxian_location[n] = LCDW-1; youbianxian_flag[n] = 0; } } middleline = (youbianxian_location[n] + zuobianxian_location[n]) / 2; } if(Bin_Image[n][LCDW/2]==0) { for(m = middleline-50; m >= 1; m --) { if(Bin_Image[n][m] == 0) { zuobianxian_location[n] = m; zuobianxian_flag[n] = 1; break; } else { zuobianxian_location[n] = 1; zuobianxian_flag[n] = 0; } } for(m = middleline+50; m <= LCDW - 1; m ++) { if(Bin_Image[n][m] == 0) { youbianxian_location[n] = m; youbianxian_flag[n] = 1; break; } else { youbianxian_location[n] = LCDW-1; youbianxian_flag[n] = 0; } } middleline = (youbianxian_location[n] + zuobianxian_location[n]) / 2; } STOP=Bin_Image[119][LCDW/2]+Bin_Image[118][LCDW/2]+Bin_Image[117][LCDW/2]+Bin_Image[116][LCDW/2]+Bin_Image[115][LCDW/2]; } for(n=115;n>45;n--) { if(Bin_Image[n-1][zuobianxian_location[n]] == 1) { for(m=zuobianxian_location[n];m>=1;m--) { if(Bin_Image[n-1][m] == 0) { zuobianxian_location[n-1]= m; zuobianxian_flag[n-1] = 1; break; } else { zuobianxian_location[n-1]=1; zuobianxian_flag[n-1] = 0; } } } if(Bin_Image[n-1][zuobianxian_location[n]] == 0) { for(m=zuobianxian_location[n];m<=LCDW - 1;m++) { if(m==LCDW-1&&end_point==0) end_point=n; if(Bin_Image[n-1][m] !=0) { zuobianxian_location[n-1] = m-1; zuobianxian_flag[n-1] = 1; break; } else { zuobianxian_location[n-1] = m; zuobianxian_flag[n-1] = 0; } } } if(Bin_Image[n-1][youbianxian_location[n]] !=0) { for(m=youbianxian_location[n];m<=LCDW - 1;m++) { if(Bin_Image[n-1][m] == 0) { youbianxian_location[n-1]= m; youbianxian_flag[n-1] = 1; break; } else { youbianxian_location[n-1]=LCDW - 1; youbianxian_flag[n-1] = 0; } } } if(Bin_Image[n-1][youbianxian_location[n]] == 0) { for(m=youbianxian_location[n];m>=1;m--) { if(m==1&&end_point==0) end_point=n; if(Bin_Image[n-1][m] !=0) { youbianxian_location[n-1] = m+1; youbianxian_flag[n-1] = 1; break; } else { youbianxian_location[n-1] = m; youbianxian_flag[n-1] = 0; } } } centerline[n]=(youbianxian_location[n] + zuobianxian_location[n]) / 2; Bin_Image[n][centerline[n]] = 0; } } void Deviation_compute(void) { float sumofleft=0,sumofright=0; for(sint16 n=70;n>45;n--) { sumofleft += LCDW/2-zuobianxian_location[n]; sumofright += LCDW/2-youbianxian_location[n]; } deviation_demo = (sumofleft+sumofright)/10; if(end_point>=10&&end_point<40) { for(sint16 n=end_point;n<40;n++) { sumofleft += LCDW/2-zuobianxian_location[n]; sumofright += LCDW/2-youbianxian_location[n]; } Center_offset1=(sumofleft+sumofright)/(40-end_point); sumofleft=0;sumofright=0; for(sint16 n=40;n<80;n++) { sumofleft += LCDW/2-zuobianxian_location[n]; sumofright += LCDW/2-youbianxian_location[n]; } Center_offset2=(sumofleft+sumofright)/40; sumofleft=0;sumofright=0; for(sint16 n=80;n<110;n++) { sumofleft += LCDW/2-zuobianxian_location[n]; sumofright += LCDW/2-youbianxian_location[n]; } Center_offset3=(sumofleft+sumofright)/30; sumofleft=0;sumofright=0; } else if(end_point>=40&&end_point<80) { Center_offset1=0; for(sint16 n=end_point;n<80;n++) { sumofleft += LCDW/2-zuobianxian_location[n]; sumofright += LCDW/2-youbianxian_location[n]; } Center_offset2=(sumofleft+sumofright)/(80-end_point); sumofleft=0;sumofright=0; for(sint16 n=80;n<110;n++) { sumofleft += LCDW/2-zuobianxian_location[n]; sumofright += LCDW/2-youbianxian_location[n]; } Center_offset3=(sumofleft+sumofright)/30; sumofleft=0;sumofright=0; } else if(end_point>=80&&end_point<110) { Center_offset1=0; Center_offset2=0; for(sint16 n=end_point;n<110;n++) { sumofleft += LCDW/2-zuobianxian_location[n]; sumofright += LCDW/2-youbianxian_location[n]; } Center_offset3=(sumofleft+sumofright)/(110-end_point); sumofleft=0;sumofright=0; } else if(end_point<10) { for(sint16 n=end_point;n<40;n++) { sumofleft += LCDW/2-zuobianxian_location[n]; sumofright += LCDW/2-youbianxian_location[n]; } Center_offset1=(sumofleft+sumofright)/(40-end_point); sumofleft=0;sumofright=0; for(sint16 n=30;n<70;n++) { sumofleft += LCDW/2-zuobianxian_location[n]; sumofright += LCDW/2-youbianxian_location[n]; } Center_offset2=(sumofleft+sumofright)/40; sumofleft=0;sumofright=0; for(sint16 n=80;n<110;n++) { sumofleft += LCDW/2-zuobianxian_location[n]; sumofright += LCDW/2-youbianxian_location[n]; } Center_offset3=(sumofleft+sumofright)/30; sumofleft=0;sumofright=0; } Center_offset = Center_offset1 * 0 + Center_offset2 * 1 + Center_offset3 * 0; deviation_demo = Center_offset; } void MotorInit(void) { gpio_init(P21_2, GPO, GPIO_LOW, GPO_PUSH_PULL); gpio_init(P21_4, GPO, GPIO_LOW, GPO_PUSH_PULL); pwm_init(ATOM0_CH1_P21_3, 17000, 0); pwm_init(ATOM0_CH3_P21_5, 17000, 0); pwm_init(ATOM1_CH1_P33_9, 50, 0); } void Motor_control(int32 motor1,int32 motor2) { if (motor1>0) { pwm_set_duty(ATOM0_CH1_P21_3,motor1); gpio_set_level(P21_2, 1); } else{ pwm_set_duty(ATOM0_CH1_P21_3,(0-motor1)); gpio_set_level(P21_2, 0); } if (motor2>0) { pwm_set_duty(ATOM0_CH3_P21_5,motor2); gpio_set_level(P21_4, 0); } else{ pwm_set_duty(ATOM0_CH3_P21_5,(0-motor2)); gpio_set_level(P21_4, 1); } if(motor1>8000) motor1=8000; if(motor1<-8000) motor1=-8000; if(motor2>8000) motor2=8000; if(motor2<-8000) motor2=-8000; } void ServoCtrl (uint32 duty) { // if (duty >= 1635) //限制幅值 // duty = 1635; // else if (duty <= 1235) //限制幅值 // duty = 1235; pwm_set_duty(ATOM1_CH1_P33_9, duty); } // //float constrain_float(float amt, float low, float high) //{ // return ((amt)<(low)?(low):((amt)>(high)?(high):(amt))); //} // //float Inc_kp = 30.0; //float Inc_ki = 0.0; float Loc_kp = 1.1f;//1.01,1.05 float Loc_kd = 0.05f;//0.015 float Loc_pd(float error) { float Loc_out; Loc_out = error * Loc_kp - imu660ra_gyro_z * Loc_kd; return Loc_out; } pid_param_t Lpid,Rpid; float constrain_float(float amt, float low, float high) { return ((amt)<(low)?(low):((amt)>(high)?(high):(amt))); } void PidInit(pid_param_t * pid ,float Kp,float Ki,float Kd,int out_max) { pid->Kp= Kp; pid->Ki= Ki; pid->Kd= Kd; pid->imax= out_max; pid->out_p = 0; pid->out_i= 0; pid->out_d= 0; pid->out = 0; pid->integrator= 0; pid->last_error= 0; pid->last_derivative = 0; } float PidIncCtrl(pid_param_t * pid, float error) { pid->out_p = pid->Kp * (error - pid->last_error); pid->out_i = pid->Ki * error; pid->out_d = pid->Kd * ((error - pid->last_error) - pid->last_derivative); pid->last_derivative = error - pid->last_error; pid->last_error = error; pid->out += pid->out_p + pid->out_i + pid->out_d; return constrain_float(pid->out, -pid->imax, pid->imax); } int16 lspeed = 0; int16 rspeed = 0; int16 LMotor=0,RMotor=0,Velocity=0,speed=200; float TMotor = 0; char START=0; int16 ENCODER=0,offset0=0,en_distance=0,en_distance_init=0; void pit(void) { imu660ra_get_gyro(); rspeed = encoder_get_count(TIM5_ENCODER); encoder_clear_count(TIM5_ENCODER); lspeed = -encoder_get_count(TIM4_ENCODER); encoder_clear_count(TIM4_ENCODER); LMotor=PidIncCtrl(&Lpid,(Velocity-absolute(deviation_demo*0.3)-(int)TMotor)-lspeed); RMotor=PidIncCtrl(&Rpid,(Velocity-absolute(deviation_demo*0.3)+(int)TMotor)-rspeed); //LMotor=PidIncCtrl(&Lpid,(Velocity-lspeed)); //RMotor=PidIncCtrl(&Rpid,(Velocity-rspeed)); ENCODER=rspeed; if(offset0==1) { en_distance+=ENCODER; en_distance_init=en_distance/111; } TMotor = Loc_pd(deviation_demo); //LMotor += LInc_pi(Velocity-lspeed-TMotor-absolute(deviation_demo*0.1));//0.1 //RMotor += RInc_pi(Velocity-rspeed+TMotor-absolute(deviation_demo*0.1)); // if(START==1) // { // Velocity=speed-80; //// if(lspeed<30 && rspeed<30) //// { //// if (LMotor>1000) //// {LMotor=1000;} //// if (RMotor>1000) //// {RMotor=1000;} //// } //// else if((lspeed>30 && rspeed>30)) //// { //// START=2; //// } // } // if(START==1) // { // Inc_kp = 8.0; // Inc_ki = 2.0; // //offset0=1; // // Velocity=speed-(80-(rspeed+lspeed)); // Velocity=speed-50; //// if(en_distance_init>=50) //// { //// START=2; //// } // if(lspeed>80 && rspeed>80) // { // START=2; // } // // // } // // if(START==2) // { // Inc_kp = 5.0; // Inc_ki = 20.0; // offset0=0; // en_distance_init=0; // ENCODER=0; // en_distance=0; // Velocity=speed; // } // if(STOP==0) { Velocity=0; //speed=0; LMotor=0; RMotor=0; TMotor=0; } if(LMotor >8000) LMotor =8000; if(RMotor> 8000) RMotor =8000; if(LMotor<-8000) LMotor=-8000; if(RMotor<-8000) RMotor=-8000; Motor_control(LMotor,RMotor);// // Motor_control(1000-deviation_demo*15-absolute(deviation_demo*2),1000+deviation_demo*15-absolute(deviation_demo*2)); //Motor_control(1000,1000); ServoCtrl(10000); } float absolute(float error2) { float real_error2; if(error2>0) real_error2= error2; else real_error2=-error2; return real_error2; } unsigned char data_to_send[64]; void ANO_DT_send_int16byte16(short data1, short data2, short data3, short data4, short data5, short data6, short data7, short data8 ){ unsigned char _cnt=0; unsigned char sum = 0, i = 0; data_to_send[_cnt++] = 0xAA; data_to_send[_cnt++] = 0xAA; data_to_send[_cnt++] = 0xF1; data_to_send[_cnt++] = 16; data_to_send[_cnt++]=(unsigned short)(data1>>8); data_to_send[_cnt++]=(unsigned char)(data1); data_to_send[_cnt++]=(unsigned short)(data2>>8); data_to_send[_cnt++]=(unsigned char)(data2); data_to_send[_cnt++]=(unsigned short)(data3>>8); data_to_send[_cnt++]=(unsigned char)(data3); data_to_send[_cnt++]=(unsigned short)(data4>>8); data_to_send[_cnt++]=(unsigned char)(data4); data_to_send[_cnt++]=(unsigned short)(data5>>8); data_to_send[_cnt++]=(unsigned char)(data5); data_to_send[_cnt++]=(unsigned short)(data6>>8); data_to_send[_cnt++]=(unsigned char)(data6); data_to_send[_cnt++]=(unsigned short)(data7>>8); data_to_send[_cnt++]=(unsigned char)(data7); data_to_send[_cnt++]=(unsigned short)(data8>>8); data_to_send[_cnt++]=(unsigned char)(data8); sum = 0; for(i=0;i<_cnt;i++) sum += data_to_send[i]; data_to_send[_cnt++]=sum; uart_write_string1(UART_2,data_to_send,_cnt); } 为该代码添加十字路口识别及十字路口补线功能
时间: 2025-07-23 12:04:49 浏览: 5
<think>我们面对的任务是在现有的C代码中实现十字路口检测和车道线补全算法。结合引用内容,我们可以从图像处理和控制逻辑两个方面入手。
一、图像处理部分
根据引用[1],我们可以使用边缘检测(如Canny)来获取图像中的边缘信息。然后,根据引用[4],我们可以使用霍夫变换来检测直线,进而识别十字路口的特征(如交叉的直线)。
步骤:
1. 灰度化:将输入图像转换为灰度图。
2. 二值化:可以采用Otsu二值化(自动确定阈值)或固定阈值二值化,根据实际情况选择。
3. 边缘检测:使用Canny算子进行边缘检测,得到边缘图像。
4. 霍夫直线检测:使用霍夫变换检测直线,得到一系列直线(用极坐标表示:ρ和θ)。
5. 十字路口特征识别:十字路口通常表现为两条相互垂直的直线交叉。我们可以通过分析检测到的直线之间的角度关系(垂直即角度差接近90度)和交点位置来识别十字路口。
二、控制逻辑部分
根据引用[2]和引用[4],在控制系统中,我们可以使用PID控制。对于十字路口,我们需要补全车道线(即当十字路口导致车道线中断时,根据已有的车道线信息进行延长或补全),然后根据补全的车道线进行路径跟踪。
步骤:
1. 十字路口判断:当图像处理部分识别到十字路口特征(如检测到两条相互垂直的直线,且交点位于图像下方靠近车的位置)时,触发十字路口处理逻辑。
2. 车道线补全:在十字路口区域,我们可能无法直接检测到完整的车道线(因为车道线在路口中断)。因此,我们需要根据路口前检测到的车道线,按照原来的方向进行延长,直到越过路口(例如,延长到图像的上半部分,即路口的对面)。
3. 控制策略:在十字路口,车辆需要按照预定路径行驶(例如直行、左转或右转)。我们可以根据补全的车道线生成期望的路径,然后使用PID控制(位置式或增量式)来跟踪路径。引用[2]中提到,速度环使用增量式PID,方向环使用位置式PID。
三、具体实现思路
1. 十字路口检测算法:
a) 使用霍夫变换检测直线,得到多条直线。
b) 对直线进行分组:按照角度θ进行分组(例如,水平方向:θ在0°±10°或180°±10°;垂直方向:θ在90°±10°或270°±10°)。注意,由于霍夫变换得到的θ范围是[0, π),所以垂直方向对应θ在90°附近(即π/2附近)。
c) 计算水平直线和垂直直线的交点。如果存在多个交点,则取一个最可能的路口中心(例如,位于图像下半部分,且交点附近有足够的边缘点)。
d) 设定一个阈值,如果检测到的水平直线和垂直直线都达到一定数量(例如至少两条水平,两条垂直),并且交点位于特定区域,则判定为十字路口。
2. 车道线补全算法:
a) 在非十字路口区域,我们正常检测左右车道线(比如通过滑动窗口、拟合直线等方法)。
b) 当进入十字路口区域时,我们根据之前检测到的车道线(例如左右车道线的直线方程)进行延长。延长线会穿过十字路口的中心区域(即我们检测到的交点)并继续向图像上方延伸,直到图像边界。
c) 补全的车道线将作为车辆跟踪的参考路径。
3. 控制逻辑调整:
a) 在检测到十字路口后,车辆的控制目标从跟随当前车道线变为跟随补全的路径(即穿过路口的延长线)。
b) 方向环控制(舵机控制):使用位置式PID,输入为车辆当前位置与补全路径的横向偏差,输出为舵机转角。
c) 速度环控制(电机控制):使用增量式PID,根据路径情况调整车速(例如在路口减速通过)。
四、代码结构建议
在现有的代码框架中,我们可以在图像处理模块之后添加一个十字路口检测模块,然后在控制决策模块中根据是否检测到十字路口来选择不同的控制策略。
伪代码示例:
```c
// 图像处理
grayscale(image); // 灰度化
threshold(image, binary_image); // 二值化,可以用Otsu
canny_edge_detection(binary_image, edges); // Canny边缘检测
hough_lines(edges, lines); // 霍夫直线检测
// 十字路口检测
if (detect_crossing(lines, &crossing_center)) {
// 检测到十字路口
// 补全车道线:基于之前检测的车道线(left_lane, right_lane)进行延长,穿过crossing_center
complete_left_lane = extend_line(left_lane, crossing_center);
complete_right_lane = extend_line(right_lane, crossing_center);
// 设置当前跟踪路径为补全的车道线(例如取中间线)
target_path = get_center_line(complete_left_lane, complete_right_lane);
} else {
// 正常检测车道线
detect_lanes(edges, &left_lane, &right_lane);
target_path = get_center_line(left_lane, right_lane);
}
// 控制模块
float lateral_error = calculate_lateral_error(target_path, current_position);
float steering_angle = position_pid(lateral_error); // 位置式PID用于舵机控制
float speed_error = target_speed - current_speed;
float speed_increment = increment_pid(speed_error); // 增量式PID用于电机控制
// 执行控制
set_steering_angle(steering_angle);
set_motor_speed(current_speed + speed_increment);
```
五、注意事项
1. 霍夫变换的参数调整(如阈值、最小线段长度等)需要根据实际场景调整,以避免噪声干扰。
2. 十字路口的判定条件(直线数量、交点位置等)需要根据实际道路情况调整。
3. 补全车道线时,延长线的方法可以简单使用直线延长,也可以使用曲线拟合(如果车道线是曲线),但十字路口内通常要求直行,所以直线延长在多数情况下可行。
4. 在十字路口,由于车道线中断,传统的车道线检测方法可能会失效,因此补全逻辑很重要。
六、相关问题
§§相关问题§§
1. 如何优化霍夫变换的参数以提高十字路口检测的准确性?
2. 在车道线补全时,如何应对弯道十字路口的情况?
3. 除了使用霍夫变换,还有哪些方法可以检测十字路口?
4. 在十字路口控制中,如何确保车辆按照预定方向(直行、左转、右转)行驶?
5. 如何测试和调试十字路口检测与补全算法的性能?</think>### 十字路口识别及车道线补全算法实现
在现有C代码中实现十字路口检测和车道线补全,需要结合图像处理和控制逻辑,以下是分步实现方案:
---
#### **一、十字路口检测流程**
1. **图像预处理**
```c
// 灰度化 + Otsu二值化
cv::cvtColor(input, gray, CV_BGR2GRAY);
cv::threshold(gray, binary, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
// Canny边缘检测(引用[1])
cv::Canny(binary, edges, 45, 135); // 阈值可调
```
2. **霍夫变换检测直线**(引用[4])
```c
std::vector<cv::Vec2f> lines;
cv::HoughLines(edges, lines, 1, CV_PI/180, 80); // 参数说明:
// 1: ρ精度(像素), CV_PI/180: θ精度(弧度), 80: 累加器阈值
```
3. **十字路口特征识别**
- **关键逻辑**:
```c
for (size_t i = 0; i < lines.size(); i++) {
float rho = lines[i][0], theta = lines[i][1];
// 筛选接近水平/垂直的线(θ误差±5°内)
if (fabs(theta) < CV_PI/36 || fabs(theta - CV_PI/2) < CV_PI/36) {
// 计算交点(引用[4])
cv::Point intersection = computeIntersection(lines[i], lines[j]);
// 验证交点位置(位于图像下半部)
if (intersection.y > image_height/2) {
is_crossroad = true;
cross_point = intersection; // 保存交点
}
}
}
```
---
#### **二、车道线补全算法**
1. **补全策略**
- 当检测到十字路口时,基于路口前的车道线斜率进行延长:
$$ y = kx + b \quad \text{(延长至交点上方)} $$
- **伪代码**:
```c
if (is_crossroad) {
// 获取当前车道线参数(通过滑动窗口或最小二乘拟合)
Line left_lane = fitLane(contours_left);
Line right_lane = fitLane(contours_right);
// 延长至交点Y坐标以上
extendLine(left_lane, cross_point.y - 20);
extendLine(right_lane, cross_point.y - 20);
}
```
2. **控制逻辑衔接(PID)**(引用[2])
```c
// 方向环PID(位置式)
float pid_steering(float error) {
static float integral = 0;
integral += error;
return Kp*error + Ki*integral + Kd*(error - prev_error);
}
// 主控制循环
while (1) {
if (is_crossroad) {
// 使用补全后的车道线计算横向偏差
error = calcDeviation(extended_left, extended_right);
} else {
// 正常车道线跟踪
error = calcDeviation(current_left, current_right);
}
steering_angle = pid_steering(error);
set_steering(steering_angle);
}
```
---
#### **三、性能优化关键**
1. **误检过滤**(引用[2])
- 添加ROI约束:只关注图像下方60%区域
- 长宽比过滤:排除短线段干扰
- 角度偏差合并:±5°内视为同一直线
2. **实时性保障**
- 限制霍夫变换检测线段数量(最多50条)
- 隔帧检测:每3帧执行1次完整十字路口检测
---
#### **四、测试验证建议**
1. **场景覆盖**
- 不同光照下的十字路口图像
- 弯道衔接十字路口的复合场景
2. **评估指标**
```c
// 检测准确率 = 正确识别次数 / 总测试次数
// 控制稳定性:横向偏差均值 < 10像素
```
> **难点提示**:十字路口的阴影干扰可通过**自适应ROI**解决,参考引用[3]中的边界扫描策略。
---
### 相关问题
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